research methodology sample in case study

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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What is case study research?

Last updated

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Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

Analyze case study research

Dovetail streamlines case study research to help you uncover and share actionable insights

• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: A Source Book of New Methods , 2nd ed. , Sage , London .

Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

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Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

• Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
• Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
• Variables 101 : an introductory video covering the different types of variables that exist within research.
• Sampling 101 : an overview of the main sampling methods
• Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
• Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

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Case Studies

Case studies are a popular research method in business area. Case studies aim to analyze specific issues within the boundaries of a specific environment, situation or organization.

According to its design, case studies in business research can be divided into three categories: explanatory, descriptive and exploratory.

Explanatory case studies aim to answer ‘how’ or ’why’ questions with little control on behalf of researcher over occurrence of events. This type of case studies focus on phenomena within the contexts of real-life situations. Example: “An investigation into the reasons of the global financial and economic crisis of 2008 – 2010.”

Descriptive case studies aim to analyze the sequence of interpersonal events after a certain amount of time has passed. Studies in business research belonging to this category usually describe culture or sub-culture, and they attempt to discover the key phenomena. Example: “Impact of increasing levels of multiculturalism on marketing practices: A case study of McDonald’s Indonesia.”

Exploratory case studies aim to find answers to the questions of ‘what’ or ‘who’. Exploratory case study data collection method is often accompanied by additional data collection method(s) such as interviews, questionnaires, experiments etc. Example: “A study into differences of leadership practices between private and public sector organizations in Atlanta, USA.”

Advantages of case study method include data collection and analysis within the context of phenomenon, integration of qualitative and quantitative data in data analysis, and the ability to capture complexities of real-life situations so that the phenomenon can be studied in greater levels of depth. Case studies do have certain disadvantages that may include lack of rigor, challenges associated with data analysis and very little basis for generalizations of findings and conclusions.

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

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Guide to the ALM Capstone Project

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• Research Methods

There are a number of ways to conduct research for your capstone topic, but everyone must start with the literature review in order to learn what has already been published on your topic. Because this step is so important, we've created an entire section on this topic (please see Choosing a Topic, Literature Reviews ). 

If your research methods will include the use of human subjects, please see the section on the  Use of Human Subjects .

The following are research methods commonly used in capstone research:

Case studies. Case studies are in-depth investigations of a single individual (noteworthy sustainability leader), a group (activist), or event (United Nations Framework Convention on Climate Change). Reading prior case studies is a must to inform your design. Reading case studies may also lead you to sustainability professionals who authored the published work. These experts could become part of your own exploration of the topic. Case study is a formal research method with a specific structure. For an introduction, visit Basics of Developing a Case Study from the Free Management Library and visit MIT Sloan Learning Edge and Harvard Kennedy School for examples of quality case studies.

Interviews . You may want to conduct interviews with experts in the field on a specific topic, such as wave energy or clean water regulation. Sustainability professionals have a wealth of information and are ordinarily happy to support beginning scholars. Your capstone reader and your capstone instructor can help make introductions through their own networks. Interviews are not simple tasks. You'll need to learn how to conduct interviews in such a way that avoids bias and elicits data that can be used for analysis. For an introduction, visit General Guidelines for Conducting Research Interviews from the Free Management Library.

Surveys . Conducting a survey is another way to gather research on your topic. Ordinarily, this method is chosen when you want to gather information from a large data set. Survey design is also not a straightforward task. For an introduction, visit the Harvard University Program on Survey Research.

Program evaluation . You may also consider in-depth and detailed evaluation of an aspect of a specific organization's sustainability project to understand if and how it met its intended goals. For an introduction, visit Evaluation Activities in Organization from the Free Management Library.

Life Cycle Sustainability Assessment . As a sustainability student, it is very likely that this term is familiar to you. Understanding the environmental, social, and economic impact of our decisions is in essence what your degree is preparing you to do. Life cycle assessment is also a research method that you could employ in your capstone project. Visit the Life Cycle Initiative hosted by the UN environment for additional information.

• Course Sequencing and Timeline
• Precapstone Tutorial
• Choosing a Topic
• ENVR E-599 Past Capstone Titles and Examples
• Final Capstone Paper
• Use of Human Subjects
• ENVR 599A Consulting for Sustainability Solutions Capstone
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Case Study Research: Methods and Designs

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves…

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves observing subjects, or cases, in their natural setting, with minimal interference from the researcher.

In the case study method , researchers pose a specific question about an individual or group to test their theories or hypothesis. This can be done by gathering data from interviews with key informants.

Here’s what you need to know about case study research design .

What Is The Case Study Method?

Main approaches to data collection, case study research methods, how case studies are used, case study model.

Case study research is a great way to understand the nuances of a matter that can get lost in quantitative research methods. A case study is distinct from other qualitative studies in the following ways:

• It’s interested in the effect of a set of circumstances on an individual or group.
• It begins with a specific question about one or more cases.
• It focuses on individual accounts and experiences.

Here are the primary features of case study research:

• Case study research methods typically involve the researcher asking a few questions of one person or a small number of people—known as respondents—to test one hypothesis.
• Case study in research methodology may apply triangulation to collect data, in which the researcher uses several sources, including documents and field data. This is then analyzed and interpreted to form a hypothesis that can be tested through further research or validated by other researchers.
• The case study method requires clear concepts and theories to guide its methods. A well-defined research question is crucial when conducting a case study because the results of the study depend on it. The best approach to answering a research question is to challenge the existing theories, hypotheses or assumptions.
• Concepts are defined using objective language with no reference to preconceived notions that individuals might have about them. The researcher sets out to discover by asking specific questions on how people think or perceive things in their given situation.

They commonly use the case study method in business, management, psychology, sociology, political science and other related fields.

A fundamental requirement of qualitative research is recording observations that provide an understanding of reality. When it comes to the case study method, there are two major approaches that can be used to collect data: document review and fieldwork.

A case study in research methodology also includes literature review, the process by which the researcher collects all data available through historical documents. These might include books, newspapers, journals, videos, photographs and other written material. The researcher may also record information using video cameras to capture events as they occur. The researcher can also go through materials produced by people involved in the case study to gain an insight into their lives and experiences.

Field research involves participating in interviews and observations directly. Observation can be done during telephone interviews, events or public meetings, visits to homes or workplaces, or by shadowing someone for a period of time. The researcher can conduct one-on-one interviews with individuals or group interviews where several people are interviewed at once.

Let’s look now at case study methodology.

The case study method can be divided into three stages: formulation of objectives; collection of data; and analysis and interpretation. The researcher first makes a judgment about what should be studied based on their knowledge. Next, they gather data through observations and interviews. Here are some of the common case study research methods:

One of the most basic methods is the survey. Respondents are asked to complete a questionnaire with open-ended and predetermined questions. It usually takes place through face-to-face interviews, mailed questionnaires or telephone interviews. It can even be done by an online survey.

2. Semi-structured Interview

For case study research a more complex method is the semi-structured interview. This involves the researcher learning about the topic by listening to what others have to say. This usually occurs through one-on-one interviews with the sample. Semi-structured interviews allow for greater flexibility and can obtain information that structured questionnaires can’t.

3. Focus Group Interview

Another method is the focus group interview, where the researcher asks a few people to take part in an open-ended discussion on certain themes or topics. The typical group size is 5–15 people. This method allows researchers to delve deeper into people’s opinions, views and experiences.

4. Participant Observation

Participant observation is another method that involves the researcher gaining insight into an experience by joining in and taking part in normal events. The people involved don’t always know they’re being studied, but the researcher observes and records what happens through field notes.

Case study research design can use one or several of these methods depending on the context.

Case studies are widely used in the social sciences. To understand the impact of socio-economic forces, interpersonal dynamics and other human conditions, sometimes there’s no other way than to study one case at a time and look for patterns and data afterward.

It’s for the same reasons that case studies are used in business. Here are a few uses:

• Case studies can be used as tools to educate and give examples of situations and problems that might occur and how they were resolved. They can also be used for strategy development and implementation.
• Case studies can evaluate the success of a program or project. They can help teams improve their collaboration by identifying areas that need improvements, such as team dynamics, communication, roles and responsibilities and leadership styles.
• Case studies can explore how people’s experiences affect the working environment. Because the study involves observing and analyzing concrete details of life, they can inform theories on how an individual or group interacts with their environment.
• Case studies can evaluate the sustainability of businesses. They’re useful for social, environmental and economic impact studies because they look at all aspects of a business or organization. This gives researchers a holistic view of the dynamics within an organization.
• We can use case studies to identify problems in organizations or businesses. They can help spot problems that are invisible to customers, investors, managers and employees.
• Case studies are used in education to show students how real-world issues or events can be sorted out. This enables students to identify and deal with similar situations in their lives.

And that’s not all. Case studies are incredibly versatile, which is why they’re used so widely.

Human beings are complex and they interact with each other in their everyday life in various ways. The researcher observes a case and tries to find out how the patterns of behavior are created, including their causal relations. Case studies help understand one or more specific events that have been observed. Here are some common methods:

1. Illustrative case study

This is where the researcher observes a group of people doing something. Studying an event or phenomenon this way can show cause-and-effect relationships between various variables.

2. Cumulative case study

A cumulative case study is one that involves observing the same set of phenomena over a period. Cumulative case studies can be very helpful in understanding processes, which are things that happen over time. For example, if there are behavioral changes in people who move from one place to another, the researcher might want to know why these changes occurred.

3. Exploratory case study

An exploratory case study collects information that will answer a question. It can help researchers better understand social, economic, political or other social phenomena.

There are several other ways to categorize case studies. They may be chronological case studies, where a researcher observes events over time. In the comparative case study, the researcher compares one or more groups of people, places, or things to draw conclusions about them. In an intervention case study, the researcher intervenes to change the behavior of the subjects. The study method depends on the needs of the research team.

Deciding how to analyze the information at our disposal is an important part of effective management. An understanding of the case study model can help. With Harappa’s Thinking Critically course, managers and young professionals receive input and training on how to level up their analytic skills. Knowledge of frameworks, reading real-life examples and lived wisdom of faculty come together to create a dynamic and exciting course that helps teams leap to the next level.

Explore Harappa Diaries to learn more about topics such as Objectives Of Research , What are Qualitative Research Methods , How To Make A Problem Statement and How To Improve your Cognitive Skills to upgrade your knowledge and skills.

A step-by-step guide to causal study design using real-world data

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• Published: 19 June 2024

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• Sarah Ruth Hoffman 1 ,
• Nilesh Gangan 1 ,
• Xiaoxue Chen 2 ,
• Joseph L. Smith 1 ,
• Arlene Tave 1 ,
• Yiling Yang 1 ,
• Christopher L. Crowe 1 ,
• Susan dosReis 3 &
• Michael Grabner 1  

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Due to the need for generalizable and rapidly delivered evidence to inform healthcare decision-making, real-world data have grown increasingly important to answer causal questions. However, causal inference using observational data poses numerous challenges, and relevant methodological literature is vast. We endeavored to identify underlying unifying themes of causal inference using real-world healthcare data and connect them into a single schema to aid in observational study design, and to demonstrate this schema using a previously published research example. A multidisciplinary team (epidemiology, biostatistics, health economics) reviewed the literature related to causal inference and observational data to identify key concepts. A visual guide to causal study design was developed to concisely and clearly illustrate how the concepts are conceptually related to one another. A case study was selected to demonstrate an application of the guide. An eight-step guide to causal study design was created, integrating essential concepts from the literature, anchored into conceptual groupings according to natural steps in the study design process. The steps include defining the causal research question and the estimand; creating a directed acyclic graph; identifying biases and design and analytic techniques to mitigate their effect, and techniques to examine the robustness of findings. The cardiovascular case study demonstrates the applicability of the steps to developing a research plan. This paper used an existing study to demonstrate the relevance of the guide. We encourage researchers to incorporate this guide at the study design stage in order to elevate the quality of future real-world evidence.

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1 Introduction

Approximately 50 new drugs are approved each year in the United States (Mullard 2022 ). For all new drugs, randomized controlled trials (RCTs) are the gold-standard by which potential effectiveness (“efficacy”) and safety are established. However, RCTs cannot guarantee how a drug will perform in a less controlled context. For this reason, regulators frequently require observational, post-approval studies using “real-world” data, sometimes even as a condition of drug approval. The “real-world” data requested by regulators is often derived from insurance claims databases and/or healthcare records. Importantly, these data are recorded during routine clinical care without concern for potential use in research. Yet, in recent years, there has been increasing use of such data for causal inference and regulatory decision making, presenting a variety of methodologic challenges for researchers and stakeholders to consider (Arlett et al. 2022 ; Berger et al. 2017 ; Concato and ElZarrad 2022 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Franklin and Schneeweiss 2017 ; Girman et al. 2014 ; Hernán and Robins 2016 ; International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2022 ; International Society for Pharmacoepidemiology (ISPE) 2020 ; Stuart et al. 2013 ; U.S. Food and Drug Administration 2018 ; Velentgas et al. 2013 ).

Current guidance for causal inference using observational healthcare data articulates the need for careful study design (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Hernán and Robins 2016 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). In 2009, Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases (Cox et al. 2009 ). In 2013, Stuart et al. emphasized counterfactual theory and trial emulation, offered several approaches to address unmeasured confounding, and provided guidance on the use of propensity scores to balance confounding covariates (Stuart et al. 2013 ). In 2013, the Agency for Healthcare Research and Quality (AHRQ) released an extensive, 200-page guide to developing a protocol for comparative effectiveness research using observational data (Velentgas et al. 2013 ). The guide emphasized development of the research question, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs (Velentgas et al. 2013 ). In 2014, Girman et al. provided a clear set of steps for assessing study feasibility including examination of the appropriateness of the data for the research question (i.e., ‘fit-for-purpose’), empirical equipoise, and interpretability, stating that comparative effectiveness research using observational data “should be designed with the goal of drawing a causal inference” (Girman et al. 2014 ). In 2017 , Berger et al. described aspects of “study hygiene,” focusing on procedural practices to enhance confidence in, and credibility of, real-world data studies (Berger et al. 2017 ). Currently, the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) maintains a guide on methodological standards in pharmacoepidemiology which discusses causal inference using observational data and includes an overview of study designs, a chapter on methods to address bias and confounding, and guidance on writing statistical analysis plans (European Medicines Agency 2023 ). In addition to these resources, the “target trial framework” provides a structured approach to planning studies for causal inferences from observational databases (Hernán and Robins 2016 ; Wang et al. 2023b ). This framework, published in 2016, encourages researchers to first imagine a clinical trial for the study question of interest and then to subsequently design the observational study to reflect the hypothetical trial (Hernán and Robins 2016 ).

While the literature addresses critical issues collectively, there remains a need for a framework that puts key components, including the target trial approach, into a simple, overarching schema (Loveless 2022 ) so they can be more easily remembered, and communicated to all stakeholders including (new) researchers, peer-reviewers, and other users of the research findings (e.g., practicing providers, professional clinical societies, regulators). For this reason, we created a step-by-step guide for causal inference using administrative health data, which aims to integrate these various best practices at a high level and complements existing, more specific guidance, including those from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) (Berger et al. 2017 ; Cox et al. 2009 ; Girman et al. 2014 ). We demonstrate the application of this schema using a previously published paper in cardiovascular research.

This work involved a formative phase and an implementation phase to evaluate the utility of the causal guide. In the formative phase, a multidisciplinary team with research expertise in epidemiology, biostatistics, and health economics reviewed selected literature (peer-reviewed publications, including those mentioned in the introduction, as well as graduate-level textbooks) related to causal inference and observational healthcare data from the pharmacoepidemiologic and pharmacoeconomic perspectives. The potential outcomes framework served as the foundation for our conception of causal inference (Rubin 2005 ). Information was grouped into the following four concepts: (1) Defining the Research Question; (2) Defining the Estimand; (3) Identifying and Mitigating Biases; (4) Sensitivity Analysis. A step-by-step guide to causal study design was developed to distill the essential elements of each concept, organizing them into a single schema so that the concepts are clearly related to one another. References for each step of the schema are included in the Supplemental Table.

In the implementation phase we tested the application of the causal guide to previously published work (Dondo et al. 2017 ). The previously published work utilized data from the Myocardial Ischaemia National Audit Project (MINAP), the United Kingdom’s national heart attack register. The goal of the study was to assess the effect of β-blockers on all-cause mortality among patients hospitalized for acute myocardial infarction without heart failure or left ventricular systolic dysfunction. We selected this paper for the case study because of its clear descriptions of the research goal and methods, and the explicit and methodical consideration of potential biases and use of sensitivity analyses to examine the robustness of the main findings.

3.1 Overview of the eight steps

The step-by-step guide to causal inference comprises eight distinct steps (Fig.  1 ) across the four concepts. As scientific inquiry and study design are iterative processes, the various steps may be completed in a different order than shown, and steps may be revisited.

A step-by-step guide for causal study design

Abbreviations: GEE: generalized estimating equations; IPC/TW: inverse probability of censoring/treatment weighting; ITR: individual treatment response; MSM: marginal structural model; TE: treatment effect

Please refer to the Supplemental Table for references providing more in-depth information.

1 Ensure that the exposure and outcome are well-defined based on literature and expert opinion.

2 More specifically, measures of association are not affected by issues such as confounding and selection bias because they do not intend to isolate and quantify a single causal pathway. However, information bias (e.g., variable misclassification) can negatively affect association estimates, and association estimates remain subject to random variability (and are hence reported with confidence intervals).

3 This list is not exhaustive; it focuses on frequently encountered biases.

4 To assess bias in a nonrandomized study following the target trial framework, use of the ROBINS-I tool is recommended ( https://www.bmj.com/content/355/bmj.i4919 ).

5 Only a selection of the most popular approaches is presented here. Other methods exist; e.g., g-computation and g-estimation for both time-invariant and time-varying analysis; instrumental variables; and doubly-robust estimation methods. There are also program evaluation methods (e.g., difference-in-differences, regression discontinuities) that can be applied to pharmacoepidemiologic questions. Conventional outcome regression analysis is not recommended for causal estimation due to issues determining covariate balance, correct model specification, and interpretability of effect estimates.

6 Online tools include, among others, an E-value calculator for unmeasured confounding ( https://www.evalue-calculator.com /) and the P95 outcome misclassification estimator ( http://apps.p-95.com/ISPE /).

3.2 Defining the Research question (step 1)

The process of designing a study begins with defining the research question. Research questions typically center on whether a causal relationship exists between an exposure and an outcome. This contrasts with associative questions, which, by their nature, do not require causal study design elements because they do not attempt to isolate a causal pathway from a single exposure to an outcome under study. It is important to note that the phrasing of the question itself should clarify whether an association or a causal relationship is of interest. The study question “Does statin use reduce the risk of future cardiovascular events?” is explicitly causal and requires that the study design addresses biases such as confounding. In contrast, the study question “Is statin use associated with a reduced risk of future cardiovascular events?” can be answered without control of confounding since the word “association” implies correlation. Too often, however, researchers use the word “association” to describe their findings when their methods were created to address explicitly causal questions (Hernán 2018 ). For example, a study that uses propensity score-based methods to balance risk factors between treatment groups is explicitly attempting to isolate a causal pathway by removing confounding factors. This is different from a study that intends only to measure an association. In fact, some journals may require that the word “association” be used when causal language would be more appropriate; however, this is beginning to change (Flanagin et al. 2024 ).

3.3 Defining the estimand (steps 2, 3, 4)

The estimand is the causal effect of research interest and is described in terms of required design elements: the target population for the counterfactual contrast, the kind of effect, and the effect/outcome measure.

In Step 2, the study team determines the target population of interest, which depends on the research question of interest. For example, we may want to estimate the effect of the treatment in the entire study population, i.e., the hypothetical contrast between all study patients taking the drug of interest versus all study patients taking the comparator (the average treatment effect; ATE). Other effects can be examined, including the average treatment effect in the treated or untreated (ATT or ATU).When covariate distributions are the same across the treated and untreated populations and there is no effect modification by covariates, these effects are generally the same (Wang et al. 2017 ). In RCTs, this occurs naturally due to randomization, but in non-randomized data, careful study design and statistical methods must be used to mitigate confounding bias.

In Step 3, the study team decides whether to measure the intention-to-treat (ITT), per-protocol, or as-treated effect. The ITT approach is also known as “first-treatment-carried-forward” in the observational literature (Lund et al. 2015 ). In trials, the ITT measures the effect of treatment assignment rather than the treatment itself, and in observational data the ITT can be conceptualized as measuring the effect of treatment as started . To compute the ITT effect from observational data, patients are placed into the exposure group corresponding to the treatment that they initiate, and treatment switching or discontinuation are purposely ignored in the analysis. Alternatively, a per-protocol effect can be measured from observational data by classifying patients according to the treatment that they initiated but censoring them when they stop, switch, or otherwise change treatment (Danaei et al. 2013 ; Yang et al. 2014 ). Finally, “as-treated” effects are estimated from observational data by classifying patients according to their actual treatment exposure during follow-up, for example by using multiple time windows to measure exposure changes (Danaei et al. 2013 ; Yang et al. 2014 ).

Step 4 is the final step in specifying the estimand in which the research team determines the effect measure of interest. Answering this question has two parts. First, the team must consider how the outcome of interest will be measured. Risks, rates, hazards, odds, and costs are common ways of measuring outcomes, but each measure may be best suited to a particular scenario. For example, risks assume patients across comparison groups have equal follow-up time, while rates allow for variable follow-up time (Rothman et al. 2008 ). Costs may be of interest in studies focused on economic outcomes, including as inputs to cost-effectiveness analyses. After deciding how the outcome will be measured, it is necessary to consider whether the resulting quantity will be compared across groups using a ratio or a difference. Ratios convey the effect of exposure in a way that is easy to understand, but they do not provide an estimate of how many patients will be affected. On the other hand, differences provide a clearer estimate of the potential public health impact of exposure; for example, by allowing the calculation of the number of patients that must be treated to cause or prevent one instance of the outcome of interest (Tripepi et al. 2007 ).

3.4 Identifying and mitigating biases (steps 5, 6, 7)

Observational, real-world studies can be subject to multiple potential sources of bias, which can be grouped into confounding, selection, measurement, and time-related biases (Prada-Ramallal et al. 2019 ).

In Step 5, as a practical first approach in developing strategies to address threats to causal inference, researchers should create a visual mapping of factors that may be related to the exposure, outcome, or both (also called a directed acyclic graph or DAG) (Pearl 1995 ). While creating a high-quality DAG can be challenging, guidance is increasingly available to facilitate the process (Ferguson et al. 2020 ; Gatto et al. 2022 ; Hernán and Robins 2020 ; Rodrigues et al. 2022 ; Sauer 2013 ). The types of inter-variable relationships depicted by DAGs include confounders, colliders, and mediators. Confounders are variables that affect both exposure and outcome, and it is necessary to control for them in order to isolate the causal pathway of interest. Colliders represent variables affected by two other variables, such as exposure and outcome (Griffith et al. 2020 ). Colliders should not be conditioned on since by doing so, the association between exposure and outcome will become distorted. Mediators are variables that are affected by the exposure and go on to affect the outcome. As such, mediators are on the causal pathway between exposure and outcome and should also not be conditioned on, otherwise a path between exposure and outcome will be closed and the total effect of the exposure on the outcome cannot be estimated. Mediation analysis is a separate type of analysis aiming to distinguish between direct and indirect (mediated) effects between exposure and outcome and may be applied in certain cases (Richiardi et al. 2013 ). Overall, the process of creating a DAG can create valuable insights about the nature of the hypothesized underlying data generating process and the biases that are likely to be encountered (Digitale et al. 2022 ). Finally, an extension to DAGs which incorporates counterfactual theory is available in the form of Single World Intervention Graphs (SWIGs) as described in a 2013 primer (Richardson and Robins 2013 ).

In Step 6, researchers comprehensively assess the possibility of different types of bias in their study, above and beyond what the creation of the DAG reveals. Many potential biases have been identified and summarized in the literature (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). Every study can be subject to one or more biases, each of which can be addressed using one or more methods. The study team should thoroughly and explicitly identify all possible biases with consideration for the specifics of the available data and the nuances of the population and health care system(s) from which the data arise. Once the potential biases are identified and listed, the team can consider potential solutions using a variety of study design and analytic techniques.

In Step 7, the study team considers solutions to the biases identified in Step 6. “Target trial” thinking serves as the basis for many of these solutions by requiring researchers to consider how observational studies can be designed to ensure comparison groups are similar and produce valid inferences by emulating RCTs (Labrecque and Swanson 2017 ; Wang et al. 2023b ). Designing studies to include only new users of a drug and an active comparator group is one way of increasing the similarity of patients across both groups, particularly in terms of treatment history. Careful consideration must be paid to the specification of the time periods and their relationship to inclusion/exclusion criteria (Suissa and Dell’Aniello 2020 ). For instance, if a drug is used intermittently, a longer wash-out period is needed to ensure adequate capture of prior use in order to avoid bias (Riis et al. 2015 ). The study team should consider how to approach confounding adjustment, and whether both time-invariant and time-varying confounding may be present. Many potential biases exist, and many methods have been developed to address them in order to improve causal estimation from observational data. Many of these methods, such as propensity score estimation, can be enhanced by machine learning (Athey and Imbens 2019 ; Belthangady et al. 2021 ; Mai et al. 2022 ; Onasanya et al. 2024 ; Schuler and Rose 2017 ; Westreich et al. 2010 ). Machine learning has many potential applications in the causal inference discipline, and like other tools, must be used with careful planning and intentionality. To aid in the assessment of potential biases, especially time-related ones, and the development of a plan to address them, the study design should be visualized (Gatto et al. 2022 ; Schneeweiss et al. 2019 ). Additionally, we note the opportunity for collaboration across research disciplines (e.g., the application of difference-in-difference methods (Zhou et al. 2016 ) to the estimation of comparative drug effectiveness and safety).

3.5 Quality Control & sensitivity analyses (step 8)

Causal study design concludes with Step 8, which includes planning quality control and sensitivity analyses to improve the internal validity of the study. Quality control begins with reviewing study output for prima facie validity. Patient characteristics (e.g., distributions of age, sex, region) should align with expected values from the researchers’ intuition and the literature, and researchers should assess reasons for any discrepancies. Sensitivity analyses should be conducted to determine the robustness of study findings. Researchers can test the stability of study estimates using a different estimand or type of model than was used in the primary analysis. Sensitivity analysis estimates that are similar to those of the primary analysis might confirm that the primary analysis estimates are appropriate. The research team may be interested in how changes to study inclusion/exclusion criteria may affect study findings or wish to address uncertainties related to measuring the exposure or outcome in the administrative data by modifying the algorithms used to identify exposure or outcome (e.g., requiring hospitalization with a diagnosis code in a principal position rather than counting any claim with the diagnosis code in any position). As feasible, existing validation studies for the exposure and outcome should be referenced, or new validation efforts undertaken. The results of such validation studies can inform study estimates via quantitative bias analyses (Lanes and Beachler 2023 ). The study team may also consider biases arising from unmeasured confounding and plan quantitative bias analyses to explore how unmeasured confounding may impact estimates. Quantitative bias analysis can assess the directionality, magnitude, and uncertainty of errors arising from a variety of limitations (Brenner and Gefeller 1993 ; Lash et al. 2009 , 2014 ; Leahy et al. 2022 ).

3.6 Illustration using a previously published research study

In order to demonstrate how the guide can be used to plan a research study utilizing causal methods, we turn to a previously published study (Dondo et al. 2017 ) that assessed the causal relationship between the use of 𝛽-blockers and mortality after acute myocardial infarction in patients without heart failure or left ventricular systolic dysfunction. The investigators sought to answer a causal research question (Step 1), and so we proceed to Step 2. Use (or no use) of 𝛽-blockers was determined after discharge without taking into consideration discontinuation or future treatment changes (i.e., intention-to-treat). Considering treatment for whom (Step 3), both ATE and ATT were evaluated. Since survival was the primary outcome, an absolute difference in survival time was chosen as the effect measure (Step 4). While there was no explicit directed acyclic graph provided, the investigators specified a list of confounders.

Robust methodologies were established by consideration of possible sources of biases and addressing them using viable solutions (Steps 6 and 7). Table  1 offers a list of the identified potential biases and their corresponding solutions as implemented. For example, to minimize potential biases including prevalent-user bias and selection bias, the sample was restricted to patients with no previous use of 𝛽-blockers, no contraindication for 𝛽-blockers, and no prescription of loop diuretics. To improve balance across the comparator groups in terms of baseline confounders, i.e., those that could influence both exposure (𝛽-blocker use) and outcome (mortality), propensity score-based inverse probability of treatment weighting (IPTW) was employed. However, we noted that the baseline look-back period to assess measured covariates was not explicitly listed in the paper.

Quality control and sensitivity analysis (Step 8) is described extensively. The overlap of propensity score distributions between comparator groups was tested and confounder balance was assessed. Since observations in the tail-end of the propensity score distribution may violate the positivity assumption (Crump et al. 2009 ), a sensitivity analysis was conducted including only cases within 0.1 to 0.9 of the propensity score distribution. While not mentioned by the authors, the PS tails can be influenced by unmeasured confounders (Sturmer et al. 2021 ), and the findings were robust with and without trimming. An assessment of extreme IPTW weights, while not included, would further help increase confidence in the robustness of the analysis. An instrumental variable approach was employed to assess potential selection bias due to unmeasured confounding, using hospital rates of guideline-indicated prescribing as the instrument. Additionally, potential bias caused by missing data was attenuated through the use of multiple imputation, and separate models were built for complete cases only and imputed/complete cases.

4 Discussion

We have described a conceptual schema for designing observational real-world studies to estimate causal effects. The application of this schema to a previously published study illuminates the methodologic structure of the study, revealing how each structural element is related to a potential bias which it is meant to address. Real-world evidence is increasingly accepted by healthcare stakeholders, including the FDA (Concato and Corrigan-Curay 2022 ; Concato and ElZarrad 2022 ), and its use for comparative effectiveness and safety assessments requires appropriate causal study design; our guide is meant to facilitate this design process and complement existing, more specific, guidance.

Existing guidance for causal inference using observational data includes components that can be clearly mapped onto the schema that we have developed. For example, in 2009 Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases, corresponding to steps 6–8 of our step-by-step guide (Cox et al. 2009 ). In 2013, the AHRQ emphasized development of the research question, corresponding to steps 1–4 of our guide, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs which correspond to steps 7 and 5, respectively (Velentgas et al. 2013 ). Much of Girman et al.’s manuscript (Girman et al. 2014 ) corresponds with steps 1–4 of our guide, and the matter of equipoise and interpretability specifically correspond to steps 3 and 7–8. The current ENCePP guide on methodological standards in pharmacoepidemiology contains a section on formulating a meaningful research question, corresponding to step 1, and describes strategies to mitigate specific sources of bias, corresponding to steps 6–8 (European Medicines Agency 2023 ). Recent works by the FDA Sentinel Innovation Center (Desai et al. 2024 ) and the Joint Initiative for Causal Inference (Dang et al. 2023 ) provide more advanced exposition of many of the steps in our guide. The target trial framework contains guidance on developing seven components of the study protocol, including eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrast of interest, and analysis plan (Hernán and Robins 2016 ). Our work places the target trial framework into a larger context illustrating its relationship with other important study planning considerations, including the creation of a directed acyclic graph and incorporation of prespecified sensitivity and quantitative bias analyses.

Ultimately, the feasibility of estimating causal effects relies on the capabilities of the available data. Real-world data sources are complex, and the investigator must carefully consider whether the data on hand are sufficient to answer the research question. For example, a study that relies solely on claims data for outcome ascertainment may suffer from outcome misclassification bias (Lanes and Beachler 2023 ). This bias can be addressed through medical record validation for a random subset of patients, followed by quantitative bias analysis (Lanes and Beachler 2023 ). If instead, the investigator wishes to apply a previously published, claims-based algorithm validated in a different database, they must carefully consider the transportability of that algorithm to their own study population. In this way, causal inference from real-world data requires the ability to think creatively and resourcefully about how various data sources and elements can be leveraged, with consideration for the strengths and limitations of each source. The heart of causal inference is in the pairing of humility and creativity: the humility to acknowledge what the data cannot do, and the creativity to address those limitations as best as one can at the time.

4.1 Limitations

As with any attempt to synthesize a broad array of information into a single, simplified schema, there are several limitations to our work. Space and useability constraints necessitated simplification of the complex source material and selections among many available methodologies, and information about the relative importance of each step is not currently included. Additionally, it is important to consider the context of our work. This step-by-step guide emphasizes analytic techniques (e.g., propensity scores) that are used most frequently within our own research environment and may not include less familiar study designs and analytic techniques. However, one strength of the guide is that additional designs and techniques or concepts can easily be incorporated into the existing schema. The benefit of a schema is that new information can be added and is more readily accessed due to its association with previously sorted information (Loveless 2022 ). It is also important to note that causal inference was approached as a broad overarching concept defined by the totality of the research, from start to finish, rather than focusing on a particular analytic technique, however we view this as a strength rather than a limitation.

Finally, the focus of this guide was on the methodologic aspects of study planning. As a result, we did not include steps for drafting or registering the study protocol in a public database or for communicating results. We strongly encourage researchers to register their study protocols and communicate their findings with transparency. A protocol template endorsed by ISPOR and ISPE for studies using real-world data to evaluate treatment effects is available (Wang et al. 2023a ). Additionally, the steps described above are intended to illustrate an order of thinking in the study planning process, and these steps are often iterative. The guide is not intended to reflect the order of study execution; specifically, quality control procedures and sensitivity analyses should also be formulated up-front at the protocol stage.

5 Conclusion

We outlined steps and described key conceptual issues of importance in designing real-world studies to answer causal questions, and created a visually appealing, user-friendly resource to help researchers clearly define and navigate these issues. We hope this guide serves to enhance the quality, and thus the impact, of real-world evidence.

Data availability

No datasets were generated or analysed during the current study.

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Hoffman, S.R., Gangan, N., Chen, X. et al. A step-by-step guide to causal study design using real-world data. Health Serv Outcomes Res Method (2024). https://doi.org/10.1007/s10742-024-00333-6

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Methodology

Ai generator.

Methodology refers to the systematic study of methods used in research. It includes Research Methodology , which is the framework for conducting investigations, and Survey Methodology , which involves techniques for collecting and analyzing survey data. A key part of any methodology is the Research Question, guiding the study’s focus and direction.

What is Methodology?

Methodology refers to the systematic study of methods used in research, encompassing principles and procedures that guide scientific investigations. It includes Research Methodology, which outlines the framework for conducting studies, and Survey Methodology, which involves techniques for collecting and analyzing survey data.

Examples of Methodology

• Surveys : Distributing questionnaires to gather quantitative data from a large sample.
• Interviews : Conducting one-on-one conversations to collect detailed qualitative data.
• Focus Groups : Facilitating group discussions to explore participants’ perceptions and opinions.
• Case Studies : Performing in-depth analysis of a single subject or group to understand complex issues.
• Experiments : Implementing controlled tests to determine causal relationships between variables.
• Participant Observation : Observing and engaging with participants in their natural environment.
• Longitudinal Studies : Tracking the same individuals over an extended period to observe changes.
• Cross-Sectional Studies : Analyzing data from different groups at a single point in time.
• Content Analysis : Systematically analyzing text or media to identify patterns and themes.
• Secondary Data Analysis : Using existing data collected by others to conduct new analyses.
• Meta-Analysis : Combining results from multiple studies to draw a broader conclusion.
• Delphi Technique : Gathering expert opinions through multiple rounds of questionnaires to achieve consensus.
• Ethnography : Immersing in a community to understand its culture and practices.
• Grounded Theory : Developing theories based on data collected during the research.
• Action Research : Collaborating with participants to address a problem and implement solutions.
• Comparative Method : Comparing different groups or cases to identify similarities and differences.
• Historical Analysis : Examining historical records to understand past events and trends.
• Systematic Review : Summarizing and evaluating existing research on a specific topic.
• Descriptive Research : Describing characteristics of a population or phenomenon.
• Narrative Inquiry : Studying personal stories and experiences to gain insights.
• Visual Analysis : Analyzing visual materials such as photographs and videos.
• Experimental Design : Using control and experimental groups to test hypotheses.
• Phenomenology : Exploring individuals’ lived experiences to understand their perceptions.
• Biographical Research : Studying an individual’s life history and experiences.
• Field Experiments : Conducting experiments in natural settings.
• Survey Design : Creating and administering surveys to collect data.
• Program Evaluation : Assessing the effectiveness of a program or intervention.
• Network Analysis : Examining relationships and interactions within a network.
• Discourse Analysis : Studying language use in texts and conversations.
• Quasi-Experimental Design : Implementing studies with non-randomized control and treatment groups.

Examples of Methodology in a Sentences

• The interview methodology involved conducting in-depth, one-on-one interviews with participants.
• A survey methodology was used to gather data from a large population using structured questionnaires.
• The case study methodology focused on an in-depth analysis of a single organization.
• Ethnographic methodology involved immersing researchers in the community to observe daily activities.
• A mixed-methods approach was utilized, combining quantitative surveys and qualitative interviews.
• Experimental methodology included a control group and a treatment group to test the hypothesis.
• Participant observation was employed to understand the behaviors and interactions within the group.
• The longitudinal study methodology tracked participants over several years to observe changes.
• Content analysis was used to analyze the themes and patterns in social media posts.
• The focus group methodology gathered diverse opinions on the new product concept.
• A cross-sectional study was conducted to compare different population groups at a single point in time.
• Action research methodology involved the participants in the research process to improve practices.
• The phenomenological methodology aimed to understand individuals’ lived experiences.
• Grounded theory methodology was used to develop a theory based on data collected from participants.
• The narrative research methodology focused on the stories and personal accounts of the participants.
• Secondary data analysis involved analyzing data previously collected by other researchers.
• Delphi methodology gathered expert opinions through multiple rounds of questionnaires.
• Comparative methodology analyzed differences and similarities between two distinct groups.
• The meta-analysis methodology combined results from multiple studies to draw a comprehensive conclusion.
• Historical research methodology examined past events to understand their impact on the present.
• The survey methodology included both closed-ended and open-ended questions to capture detailed responses.
• Field experiments were conducted to test the intervention in a natural setting.
• Discourse analysis examined the language and communication patterns within the texts.
• The biographical research methodology studied individuals’ life histories and personal experiences.
• Quantitative content analysis was used to count and analyze the frequency of specific words or themes.
• Case-control study methodology compared individuals with a specific condition to those without it.
• Systematic review methodology evaluated and synthesized findings from existing research studies.
• Experimental design methodology manipulated variables to observe their effect on the outcome.
• Visual ethnography involved analyzing visual materials such as photographs and videos.
• Clinical trial methodology tested the efficacy and safety of new medical treatments through controlled experiments.

Methodology Examples in Project Proposal

1. Survey Methodology : We will distribute online surveys to 500 participants to gather quantitative data on customer satisfaction levels.

2. Interview Methodology : Conduct semi-structured interviews with 20 key stakeholders to gain insights into project requirements and expectations.

3. Focus Group Methodology : Facilitate focus groups with selected users to discuss and refine the design of the new software interface.

4 . Case Study Methodology : Analyze three case studies of similar projects to identify best practices and potential pitfalls.

5. Experimental Methodology : Implement a controlled experiment to test the impact of the new training program on employee productivity.

6. Ethnographic Methodology : Engage in participant observation within the target community for three months to understand user behavior and cultural influences.

7. Mixed Methods Approach : Combine quantitative data from surveys with qualitative insights from interviews to provide a comprehensive analysis of project outcomes.

8. Action Research Methodology : Collaborate with project team members to iteratively implement and assess improvements, ensuring continuous feedback and adaptation.

9. Content Analysis : Review and analyze project-related documents and communications to identify common themes and areas for improvement.

10. Delphi Methodology : Use the Delphi technique to gather and refine expert opinions through multiple rounds of questionnaires to achieve a consensus on project goals and strategies.

Methodology Examples in Report

Example 1: survey methodology.

In this study, we employed a survey methodology to collect data from participants. The survey was designed to gather information on consumer preferences and behaviors. The key steps in our survey methodology were as follows:

• Population : All residents of City X aged 18 and above.
• Sample Size : 500 participants selected through random sampling.
• Questionnaire : A structured questionnaire with 25 closed-ended questions.
• Pilot Testing : Conducted with 50 participants to ensure clarity and reliability of the questions.
• Mode : Online survey distributed via email.
• Duration : Data collection spanned over two weeks from January 10 to January 24, 2024.
• Software : SPSS version 26.
• Techniques : Descriptive statistics, cross-tabulations, and chi-square tests.

Example 2: Experimental Methodology

This experiment aimed to evaluate the effectiveness of a new teaching method on students’ performance. The experimental methodology comprised the following steps:

• Selection : 100 high school students from School Y.
• Grouping : Randomly assigned to control (n=50) and experimental (n=50) groups.
• Pre-test : Administered to both groups to assess initial knowledge levels.
• Intervention : The experimental group received the new teaching method, while the control group continued with the traditional method for six weeks.
• Post-test : Conducted to measure knowledge acquisition and retention.
• Teaching Aids : Interactive multimedia tools for the experimental group.
• Traditional Tools : Textbooks and lectures for the control group.
• Software : R programming language.
• Techniques : T-tests to compare pre-test and post-test scores between groups.

Example 3: Qualitative Methodology

For this research, we utilized a qualitative methodology to explore the experiences of healthcare workers during the pandemic. The methodology included:

• Selection : 30 healthcare workers from various hospitals.
• Sampling Technique : Purposive sampling to ensure diverse perspectives.
• Interviews : Semi-structured interviews conducted in-person and via Zoom.
• Duration : Each interview lasted approximately 45-60 minutes.
• Recording : With participants’ consent, interviews were a-recorded and transcribed verbatim.
• Approach : Thematic analysis.
• Software : NVivo for coding and organizing themes.
• Validation : Member checking and peer debriefing to ensure credibility.

Example 4: Case Study Methodology

In this case study, we investigated the implementation of a new software system in Company Z. The methodology involved:

• Criteria : Companies that recently implemented the software within the past year.
• Company Profile : Medium-sized company with 200 employees.
• Interviews : Conducted with key stakeholders including IT staff, managers, and end-users.
• Documents : Analysis of company reports, project plans, and user feedback forms.
• Observations : On-site visits to observe the software in use.
• Techniques : Triangulation to corroborate findings from multiple sources.
• Framework : SWOT analysis to identify strengths, weaknesses, opportunities, and threats related to the software implementation.

Example 5: Mixed-Methods Methodology

This mixed-methods study examined the impact of remote work on employee productivity and well-being. The methodology comprised both quantitative and qualitative components:

• Survey : Online survey with Likert-scale questions administered to 300 employees.
• Analysis : Regression analysis to identify factors affecting productivity.
• Focus Groups : Three focus groups with 8-10 participants each to discuss remote work experiences.
• Thematic Analysis : Coding and theme development using Atlas.ti.
• Data Triangulation : Combined findings from both quantitative and qualitative data to provide a comprehensive understanding of the impact of remote work.

Quantitative Methodology Examples

• Survey Research : Conducting a large-scale survey to collect numerical data on consumer preferences.
• Experimental Design : Implementing a controlled experiment to test the effects of a new drug on patient recovery rates.
• Cross-Sectional Study : Analyzing data from different population groups at a single point in time to identify correlations.
• Longitudinal Study : Tracking the same group of individuals over several years to observe changes in health outcomes.
• Secondary Data Analysis : Using existing datasets from government databases to analyze employment trends.
• Quasi-Experimental Design : Comparing outcomes between a group receiving an intervention and a non-randomized control group.
• Descriptive Statistics : Summarizing and describing the main features of a dataset using measures such as mean, median, and mode.
• Regression Analysis : Investigating the relationship between independent variables and a dependent variable to predict outcomes.
• Correlation Study : Measuring the strength and direction of the relationship between two variables, such as income and education level.
• Time Series Analysis : Analyzing data points collected or recorded at specific time intervals to identify trends over time.

Types of Methodology

1. qualitative methodology.

This involves collecting non-numerical data to understand concepts, opinions, or experiences. Methods include:

• Interviews : Conducting one-on-one conversations to gather detailed insights.
• Focus Groups : Facilitating group discussions to explore a specific topic.
• Observations : Watching and recording behaviors in a natural setting.

2. Quantitative Methodology

This focuses on numerical data and statistical analysis. Methods include:

• Surveys : Using questionnaires to collect data from a large number of respondents.
• Experiments : Conducting controlled tests to determine cause-and-effect relationships.
• Secondary Data Analysis : Analyzing existing data collected by other researchers.

3. Mixed Methods

This combines both qualitative and quantitative approaches. It provides a comprehensive understanding by integrating diverse data sources.

• Sequential Explanatory Design : Collecting and analyzing quantitative data first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation : Collecting both types of data simultaneously to cross-verify findings.

4. Case Study Methodology

This involves an in-depth study of a particular case within a real-world context. Methods include:

• Document Analysis : Reviewing existing documents related to the case.
• Interviews : Gathering detailed information from individuals involved in the case.
• Observations : Observing the case in its natural setting to gather contextual data.

5. Ethnographic Methodology

This focuses on studying cultures and communities. Methods include:

• Participant Observation : Engaging with the community while observing their behaviors and interactions.
• Field Notes : Recording detailed notes of observations and experiences in the field.
• Interviews : Conducting interviews with community members to gain deeper insights.

Each of these methodologies provides a different approach to research, helping researchers to choose the most appropriate method for their specific study objectives.

Importance of Methodology in Research

1. ensures research validity and reliability.

• Validity : Methodology ensures that the research measures what it is intended to measure. It guarantees that the results accurately represent the phenomenon being studied.
• Reliability : It ensures consistency in the research results. Reliable methodologies produce stable and consistent results over repeated trials.

2. Provides a Clear Research Framework

• Structured Process : Methodology provides a detailed plan outlining the steps involved in the research process. This structure helps researchers stay organized and focused.
• Replicability : A well-defined methodology allows other researchers to replicate the study, verifying results and contributing to the body of knowledge.

3. Enhances Credibility and Objectivity

• Transparency : Clearly documenting the research methodology enhances the transparency of the study, allowing others to understand how data was collected and analyzed.
• Objectivity : By following a systematic approach, methodology minimizes biases and ensures objective analysis and interpretation of data.

4. Facilitates Data Collection and Analysis

• Appropriate Tools and Techniques : Methodology helps in selecting the most suitable tools and techniques for data collection and analysis, ensuring accurate and relevant data is gathered.
• Efficient Analysis : With a clear methodological framework, data analysis becomes more efficient, leading to valid conclusions and insights.

5. Supports Theory Development and Hypothesis Testing

• Theory Development : Methodologies, particularly in qualitative research, help in developing new theories based on observed patterns and themes.
• Hypothesis Testing : In quantitative research, methodologies are crucial for testing hypotheses, allowing researchers to confirm or refute their assumptions.

Synonyms of Methodology

How to write a methodology, 1. introduction.

Begin with a brief overview of the research problem and objectives. Explain why the chosen methodology is appropriate for addressing the research question.

2. Research Design

Describe the overall approach of your study:

• Qualitative , Quantitative , or Mixed Methods .
• Provide a rationale for your choice.

3. Data Collection Methods

Detail the specific methods you will use to collect data:

• Surveys : Include information about the type of survey, sample size, and how respondents are selected.
• Interviews : Describe the format (structured, semi-structured, or unstructured), and the selection process for participants.
• Observations : Explain what will be observed, the context, and how observations will be recorded.
• Experiments : Outline the experimental design, control variables, and the procedure.

4. Data Analysis Methods

Explain how you will analyze the collected data:

• Quantitative Analysis : Statistical tests, software used, and how you will ensure reliability and validity.
• Qualitative Analysis : Coding processes, thematic analysis, or other methods used to interpret data.

5. Sampling

Describe your sampling strategy:

• Population : Define the population from which your sample will be drawn.
• Sample Size : Justify the size of your sample.
• Sampling Technique : Explain whether you will use random sampling, stratified sampling, convenience sampling, etc.

6. Ethical Considerations

Detail how you will address ethical issues:

• Informed Consent : How you will obtain and document consent from participants.
• Confidentiality : Measures to protect the privacy of participants.
• Approval : Mention any institutional review board (IRB) or ethics committee approvals.

7. Limitations

Acknowledge potential limitations of your methodology:

• Discuss possible weaknesses and how they may impact your results.
• Explain steps you will take to mitigate these limitations.

8. Conclusion

Summarize the key points of your methodology. Reinforce why your chosen methods are the best fit for your research objectives.

FAQ’s

What are qualitative methods.

Qualitative methods involve non-numerical data collection, like interviews and observations, to understand concepts, opinions, or experiences.

What are quantitative methods?

Quantitative methods involve numerical data collection and statistical analysis to identify patterns, relationships, or trends.

What is a mixed-methods approach?

A mixed-methods approach combines qualitative and quantitative methods to provide a comprehensive analysis.

How do you choose a methodology?

Choosing a methodology depends on the research question, objectives, and the type of data needed.

What is a research design?

Research design is the framework that guides the collection and analysis of data, ensuring the research question is effectively addressed.

What is the difference between methodology and methods?

Methodology refers to the overall approach and rationale, while methods are specific techniques used for data collection and analysis.

What is a case study?

A case study is an in-depth examination of a particular instance, event, or individual to explore or illustrate broader principles.

What is an experiment in research?

An experiment involves manipulating variables to determine their effect on other variables, establishing cause-and-effect relationships.

What is a survey?

A survey is a data collection method using questionnaires or interviews to gather information from a large group.

What is sampling in research?

Sampling is selecting a subset of a population to represent the whole, ensuring the study’s findings are generalizable.

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NASA is moving forward with 10 studies to examine more affordable and faster methods of bringing samples from Mars’ surface back to Earth as part of the agency’s Mars Sample Return Program. As part of this effort, NASA will award a firm-fixed-price contract for up to $1.5 million to conduct 90-day studies to seven industry proposers.

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“Mars Sample Return will be one of the most complex missions NASA has undertaken, and it is critical that we carry it out more quickly, with less risk, and at a lower cost,” said NASA Administrator Bill Nelson. “I’m excited to see the vision that these companies, centers and partners present as we look for fresh, exciting, and innovative ideas to uncover great cosmic secrets from the Red Planet.”

Over the last quarter century, NASA has engaged in a systematic effort to determine the early history of Mars and how it can help us understand the formation and evolution of habitable worlds, including Earth. As part of that effort, Mars Sample Return has been a long-term goal of international planetary exploration for the past two decades. NASA’s Perseverance rover has been collecting samples for later collection and return to Earth since it landed on Mars in 2021.

The following companies and proposals were selected from among those that responded to an April 15 request for proposals:

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• Aerojet Rocketdyne in Huntsville, Alabama: “A High-Performance Liquid Mars Ascent Vehicle, Using Highly Reliable and Mature Propulsion Technologies, to Improve Program Affordability and Schedule”
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• Northrop Grumman in Elkton, Maryland: “High TRL MAV Propulsion Trades and Concept Design for MSR Rapid Mission Design”
• Whittinghill Aerospace in Camarillo, California: “A Rapid Design Study for the MSR Single Stage Mars Ascent Vehicle”

NASA’s Mars Sample Return is a strategic partnership with ESA (the European Space Agency). Returning scientifically selected samples to Earth for study using the most sophisticated instruments around the world can revolutionize our understanding of Mars and would fulfill one of the highest priority solar system exploration goals as identified by the National Academies of Science, Engineering and Medicine.

For more information on Mars Sample Return, visit:

https://science.nasa.gov/mission/mars-sample-return/

Dewayne Washington Headquarters, Washington 202-358-1600 [email protected]

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• Published: 17 June 2024

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging

• Chao-Jie Ye   ORCID: orcid.org/0009-0009-7565-5048 1 , 2   na1 ,
• Dong Liu 1 , 2   na1 ,
• Ming-Ling Chen   ORCID: orcid.org/0000-0001-7992-1838 1 , 2   na1 ,
• Li-Jie Kong 1 , 2 ,
• Chun Dou 1 , 2 ,
• Yi-Ying Wang   ORCID: orcid.org/0000-0002-1252-7788 1 , 2 ,
• Min Xu   ORCID: orcid.org/0000-0003-3930-8718 1 , 2 ,
• Yu Xu 1 , 2 ,
• Mian Li   ORCID: orcid.org/0000-0001-6514-2729 1 , 2 ,
• Zhi-Yun Zhao   ORCID: orcid.org/0000-0001-5950-2732 1 , 2 ,
• Rui-Zhi Zheng 1 , 2 ,
• Jie Zheng 1 , 2 ,
• Jie-Li Lu   ORCID: orcid.org/0000-0003-1317-0896 1 , 2 ,
• Yu-Hong Chen 1 , 2 ,
• Guang Ning 1 , 2 ,
• Wei-Qing Wang   ORCID: orcid.org/0000-0001-6027-3084 1 , 2 ,
• Yu-Fang Bi   ORCID: orcid.org/0000-0002-4829-5915 1 , 2 &
• Tian-Ge Wang   ORCID: orcid.org/0000-0003-0723-489X 1 , 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Health policy
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Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms ( n  = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity ( n  = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z -score higher) was causally associated with a higher aging-GIP ( β [95% confidence interval (CI)] in different models ranging from 1.00 [0.82–1.18] to 1.07 [0.91–1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience ( β [95% CI] ranging from 0.97 [0.82–1.12] to 1.04 [0.91–1.17] s.d.), self-rated health (0.61 [0.43–0.79] to 0.76 [0.59–0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02–1.48] to 1.35 [1.11–1.65]) and parental lifespan (1.77 [0.010–3.54] to 2.95 [1.13–4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

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Genome-wide association studies

Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk

Data availability.

All GWAS summary statistics analysed in this study are publicly available as shown in Table 1 and Supplementary Table 1 for download by qualified researchers. The GWAS data for mental well-being traits can be obtained from the GWAS catalogue 38 ( https://www.ebi.ac.uk/gwas/publications/30643256 ). The GWAS data for aging phenotypes can be retrieved or requested from the study authors at https://doi.org/10.7488/ds/2972 (the aging-GIP 14 ), https://doi.org/10.6084/m9.figshare.9204998.v3 (frailty index 42 ), http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006620 (self-rated health 43 ), https://doi.org/10.5281/zenodo.1302861 (healthspan 44 ), https://doi.org/10.7488/ds/2463 (parental lifespan 45 ) and https://www.longevitygenomics.org/downloads (longevity 46 ). All data generated in this study are included in the Supplementary Information .

Code availability

All the MR analyses were conducted using R packages TwoSampleMR (version 0.5.7), MVMR (version 0.4), MRPRESSO (version 1.0) and MRlap (version 0.0.3.0) in R software (version 4.3.1). Custom code that supports the findings of this study is available at https://github.com/yechaojie/mental_aging .

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Acknowledgements

This work was supported by the grants from the National Natural Science Foundation of China (82370820, 82088102, 91857205, 823B2014 and 81930021), the ‘Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support’ from Shanghai Jiao Tong University School of Medicine (20171901 Round 2), and the Innovative Research Team of High-level Local Universities in Shanghai. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to the participants of all the GWASs used in this manuscript and the investigators who made these GWAS data publicly available.

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Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Chao-Jie Ye, Dong Liu, Ming-Ling Chen, Li-Jie Kong, Chun Dou, Yi-Ying Wang, Min Xu, Yu Xu, Mian Li, Zhi-Yun Zhao, Rui-Zhi Zheng, Jie Zheng, Jie-Li Lu, Yu-Hong Chen, Guang Ning, Wei-Qing Wang, Yu-Fang Bi & Tian-Ge Wang

Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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C.-J.Y. and T.-G.W. contributed to the conception and design of the study. C.-J.Y. performed statistical analyses and drafted the manuscript. T.-G.W. critically revised the manuscript. D.L., M.-L.C. and T.-G.W. checked the statistical analysis and proofread the manuscript. T.-G.W., G.N., W.-Q.W. and C.-J.Y. obtained funding. All authors contributed to the acquisition or interpretation of data, proofreading of the manuscript for important intellectual content and the final approval of the version to be published. T.-G.W. is the guarantor of this work and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Wei-Qing Wang , Yu-Fang Bi or Tian-Ge Wang .

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Ye, CJ., Liu, D., Chen, ML. et al. Mendelian randomization evidence for the causal effect of mental well-being on healthy aging. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01905-9

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• Published: 20 June 2024

Developing survey weights to ensure representativeness in a national, matched cohort study: results from the children and young people with Long Covid (CLoCk) study

• Natalia K Rojas 1 ,
• Bianca L De Stavola 2 ,
• Tom Norris 1 ,
• Mario Cortina-Borja 2 ,
• Manjula D Nugawela 2 ,
• Dougal Hargreaves 3 ,
• Emma Dalrymple 2 ,
• Kelsey McOwat 4 ,
• Ruth Simmons 4 ,
• Terence Stephenson 2 ,
• Roz Shafran 2 ,
• CLoCk Consortium &
• Snehal M Pinto Pereira 1  

BMC Medical Research Methodology volume  24 , Article number:  134 ( 2024 ) Cite this article

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Findings from studies assessing Long Covid in children and young people (CYP) need to be assessed in light of their methodological limitations. For example, if non-response and/or attrition over time systematically differ by sub-groups of CYP, findings could be biased and any generalisation limited. The present study aimed to (i) construct survey weights for the Children and young people with Long Covid (CLoCk) study, and (ii) apply them to published CLoCk findings showing the prevalence of shortness of breath and tiredness increased over time from baseline to 12-months post-baseline in both SARS-CoV-2 Positive and Negative CYP.

Logistic regression models were fitted to compute the probability of (i) Responding given envisioned to take part, (ii) Responding timely given responded, and (iii) (Re)infection given timely response. Response, timely response and (re)infection weights were generated as the reciprocal of the corresponding probability, with an overall ‘envisioned population’ survey weight derived as the product of these weights. Survey weights were trimmed, and an interactive tool developed to re-calibrate target population survey weights to the general population using data from the 2021 UK Census.

Flexible survey weights for the CLoCk study were successfully developed. In the illustrative example, re-weighted results (when accounting for selection in response, attrition, and (re)infection) were consistent with published findings.

Conclusions

Flexible survey weights to address potential bias and selection issues were created for and used in the CLoCk study. Previously reported prospective findings from CLoCk are generalisable to the wider population of CYP in England. This study highlights the importance of considering selection into a sample and attrition over time when considering generalisability of findings.

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By March 2022, most children and young people (CYP) in the United Kingdom (UK) appeared to have been exposed to SARS-CoV-2, with antibodies found in 82% and 99% of primary and secondary school aged pupils, respectively [ 1 ]. Given the scale of infection, a substantial number could develop symptoms of Long Covid (also referred to as Post Covid Condition). Long Covid in CYP can be defined as the presence of one or more impairing, persisting, physical symptom(s) lasting 12 or more weeks after initial SARS-CoV-2 infection that may fluctuate or relapse, either continuing or developing post-infection [ 2 ]. Hence, it is important to study Long Covid, particularly given its potential impact on healthcare systems and need for planning.

Systematic reviews demonstrate that common symptoms of Long Covid in CYP at 3 months post-testing/infection include fatigue, insomnia, loss of smell, and headaches [ 3 ]. The Long Covid (CLoCk) study, is the largest matched cohort study of Long Covid in CYP in the world [ 4 ]. Based in England, CLoCk collected data on over 30,000 CYP testing positive and negative between September 2020 and March 2021 over a two-year period. CLoCk followed 6,804 CYP 3 months after a SARS-CoV-2 PCR-test and found over half of CYP testing negative and 67% of those testing positive reported at least one symptom 3-months post-testing [ 5 ]. The most common symptoms amongst test positives were tiredness (39%), headache (23%) and shortness of breath (23%), with test negatives reporting mainly tiredness (24%) and headache (14%). Results from this, and all other studies, need to be assessed against their methodological limitations, two of which are considered here. First, response rates to study invitation are generally low, for example, the response rate at the 3-months post-testing sweep of the CLoCk study was 13.4% [ 5 ]. Similarly, the UK Office for National Statistics’ [ 6 ] COVID-19 infection survey had a response rate of 12%. Second, all longitudinal studies suffer from attrition over time [ 7 ] which is typically more pronounced in studies with longer follow-up periods [ 8 ].

If non-response and attrition over time systematically differ by sub-groups in the envisioned population, findings could be biased and attempts to generalise findings to the wider population limited [ 9 , 10 , 11 ]. For example, those with particular characteristics (e.g., older, females and from specific ethnicities) are more likely to positively respond to study invitation [ 12 ]. Reasons for attrition over time include study withdrawal, individuals becoming uncontactable [e.g., due to change in contact details; 13 ] or lacking motivation to continue participating. Indeed, both initial non-respondents and those lost to follow-up are often socioeconomically disadvantaged and less healthy [ 14 ]. With studies on Long Covid, particularly those comparing test-positives to test-negatives, an additional source of bias could exist. For example, within the CLoCk study, to isolate the effect of Long Covid from that of living through a pandemic, researchers originally excluded from the analytic sample those (re)infected, that is, test-negatives who subsequently tested positive and test-positive CYP who were subsequently reinfected [ 15 ]. This criterion yields a cohort of CYP who, as per the data available, appear to have either (i) always tested negative, or (ii) tested positive only once. However, these CYP may not be representative of the larger population of CYP in England. One well-established method to assess the impact of potential bias due to non-response, attrition and sample selection is weighting, that is, emphasising the contribution of some individuals over others in an analysis to reconstruct the target population and/or general population [ 9 ]. Such weighting methodology is appropriate when data are missing (due to non-response, attrition, and sample selection) at random [ 16 ], that is, the missingness is dependent on fully observed characteristics such as sex, age, socioeconomic disadvantage and health status. Yet, this powerful statistical technique to address potential selection biases has been underutilised in epidemiological research [ 9 ].

In this manuscript we construct weights for the CLoCk study [ 17 ] and, as an illustrative example, apply them to published findings showing the overall prevalence of shortness of breath and tiredness increases in CYP from baseline (i.e., at the time of their index PCR test) to 12-months post-baseline [ 15 ]. Specifically, to assess the robustness of conclusions drawn from CLoCk data about Long Covid’s symptomatology and trajectory in CYP, the present study aims to (i) create weights for the CLoCk study at its data collection sweeps 3-, 6- and 12-months post-index PCR-test, and (ii) apply developed weights to the analysis of shortness of breath and tiredness over a 12-month period to determine whether accounting for any biases in response, attrition or (re)infection affects published results.

The CLoCk study identified 219,175 CYP (91,014 SARS-CoV-2 Positive and 128,161 SARS-CoV-2 Negative) who had a SARS-CoV-2 PCR-test between September 2020 and March 2021 through the UK Health Security Agency’s (UKHSA) database containing the outcomes of all such tests. At study invitation, test-positives were matched to test-negatives on age, sex, region of residence and month of test. Consenting SARS-CoV-2 Positive and Negative CYP complete a questionnaire about their mental and physical health 3-, 6-, 12- and 24-months post-index PCR-test [ 4 ]. Of note, the sweeps of data collection depend on the CYP’s month of test, with 3-, 6-, 12-, and 24-month data available for some (tested in January-March 2021), while for others only 6-, 12-, and 24-month (tested in October-December 2020), or 12- and 24-month (tested in September 2020 and an additional cohort from December 2020) data were collected. This manuscript is based on all data collected for the 3-, 6-, and 12-month timepoints. The analytic samples for previous CLoCk publications [ 5 , 15 , 18 ] were such that: (i) CYP must have responded within a pre-specified timeframe (i.e., < 24, ≤34, and ≤ 60 weeks post-testing for the 3-, 6-, and 12-month questionnaires, respectively) and (ii) Initial SARS-CoV-2 Negative CYP must have never reported a positive test, with initial SARS-CoV-2 Positive CYP never reporting being reinfected. The latter requirement was determined using a combination of self-report and UKHSA held data. See Figs.  1 and 2 for exclusion criteria at each stage and participant flow.

Logic model for inclusion in the analytic sample at 3-, 6-, and 12-months

a Initially, due to funding constraints, only a portion of those tested in December 2020 were contacted to participate at 6 months. Hence, some children and young people tested in December 2020 provided both 6- and 12- month data, whereas others only 12-month data

b Determined through self-report and UKHSA data. (Re)infected refers to (i) a SARS-CoV-2 Negative subsequently testing positive, or (ii) a SARS-CoV-2 Positive testing positive again

Flow diagram of participants at 3-, 6-, and 12 months

a Determined using the following cut off points: < 24 weeks post-testing for the 3-month questionnaire; ≤ 34 weeks post-testing for the 6-month questionnaire; ≤ 60 weeks post-testing for the 12-month questionnaire

c By definition of a COVID positive episode [ 19 ], a test-positive person cannot be reinfected by 3 months

Research ethics approval was granted by the Yorkshire and The Humber—South Yorkshire Research Ethics Committee (REC reference: 21/YH/0060; IRAS project ID:293,495).

Index COVID status, age, sex and region were determined from data held at UKHSA. Socioeconomic status was proxied using the Index of Multiple Deprivation (IMD), obtained using CYP’s lower super output area (i.e., small local area level-based geographic hierarchy), where higher values are indicative of lower deprivation [ 20 ]. Ethnicity was self-reported and collected at registration. Current (i.e., at time of questionnaire completion) health, current loneliness, and number of symptoms being experienced, including tiredness and shortness of breath, [out of a possible 21, consistent with the ISARIC Paediatric Working Group; 5 ] were self-reported at each data collection sweep. Similarly, standardised measures were collected, including the: Short Warwick and Edinburgh Mental Wellbeing Scale [SWEMWS; 21 ]; EuroQol Visual Analogue Scale [EQ-VAS; 22 ], EQ-5D-Y [ 23 ], Strengths and Difficulties Questionnaire [SDQ; 24 ], UCLA Loneliness Scale [ 25 ], and Chalder Fatigue Scale [CFS; 26 ]. See Additional File 1 : Table  1 for further information.

For each data collection sweep, three indicator variables were created:

Responding given envisioned to take part (Yes/No): If participants completed the whole questionnaire.

Responding timely given responded (Yes/No): If participants who responded, responded to the questionnaire < 24 weeks post-testing (3-month questionnaire); ≤ 34 weeks post-testing (6-month questionnaire) and ≤ 60 weeks post-testing (12-month questionnaire).

(Re)infected given timely response (Yes/No): ‘Yes’ indicates, among those responding timely, SARS-CoV-2 index-test Positives that were reinfected and SARS-CoV-2 index-test Negatives that subsequently tested positive. ‘No’ indicates, among those responding timely, initial SARS-CoV-2 Positives that never report another positive test and initial SARS-CoV-2 Negatives that never report a positive test. A combination of the UKHSA’s testing data and self-reported information on having ever tested positive was used to generate this.

In total nine indicator variables were created: three at each data collection sweep.

Analyses were conducted using Stata v17 [ 27 ].

Weight generation

At each data collection sweep and corresponding to the three indicator variables created (as described above), three ‘mini’ survey weights were generated to account for CYP being lost either due to (i) non-response, (ii) responding after the established cut-off points or (iii) (re)infection with SARS-CoV-2. A fourth, combined ‘envisioned population’ weight was created which accounted for loss in the analytic sample due to all three factors. These four survey weights (three ‘mini’ survey weights and one ‘envisioned population’ weight) were generated for each data collection sweep, (i.e., 3-, 6- and 12-months post-SARS-CoV-2 test), see Fig.  3 for details.

Steps in weight generation

Here, the term ‘envisioned’ population refers to all CYP that could have taken part at the relevant time point (i.e., it is the maximum number of CYP that could provide data at a specific time point and was 50,845, 127,894 and 219,175 at 3-, 6-, and 12-months respectively). The ‘target’ population varies depending on the specific research question. For example, in the illustrative example described below, the target population is all CYP that could have taken part at 6 months (i.e., N  = 127,894; see Fig.  4 ).

Participant flow in the published CLoCk study [ 15 ] to be replicated

a Here, the target population is all children and young people that could have taken part at 6 months

b A late response at 6 months is defined as not responding ≤ 34 weeks post-testing

c Determined through self-report and UKHSA data. (Re)infected refers to (i) a SARS-CoV-2 Negative subsequently testing positive, or (ii) a SARS-CoV-2 Positive testing positive again

d A late response at 12 months is defined as not responding ≤ 60 weeks post-testing

e Of these, 1,826 children and young people registered at 3 months (806 SARS-CoV-2 Negative and 1,020 SARS-CoV-2 Positive)

The three ‘mini’ survey weights were calculated for (i) response given envisioned to take part, (ii) timely response given response, and (iii) (re)infection given timely response. Each ‘mini’ survey weight was calculated as the reciprocal of its corresponding conditional probability (Fig.  3 ). These conditional probabilities were computed using logistic regression (described below).

For the logistic regression of responding given envisioned to take part, all available data (held at UKHSA for study-design matching) and pair-wise interactions were considered as explanatory variables. For the logistic regressions of (i) responding timely given responded and (ii) (re)infected given timely response, questionnaire data was also available for use as predictors. Forward ( p <  0.157) and backward ( p <  0.200) stepwise selection processes were used to refine models used to predict these probabilities with cut-offs selected as per recommendations [ 28 ]. Our weighting approach is appropriate when data are missing at random [ 16 ]. In an attempt to ensure this assumption is valid we included sex, age, region, index COVID Status and IMD in all but one (see below) of the logistic regression models. Of these, age and IMD were continuous variables, while the others were categorical. We determined the appropriate functional form for the relationship between age/IMD and the log odds of the probability of the (three) outcomes by modelling the relationship (i) linearly, (ii) categorically (age: 11–13, 14–15, 16–17 years; IMD deciles, 1–5), (iii) with linear and quadratic terms and (iv) using fractional polynomials with up to two degrees. The functional forms with the lowest Akaike’s information criterion (i.e., the best fitting model) were used in our subsequent models. Importantly, index COVID Status was excluded as a predictor of the probability of being (re)infected given CYP responded timely at 3 months. This is because, by definition of a COVID positive episode [ 19 ], once a person tests positive, they would only be considered to be reinfected should they test positive more than 3 months after the initial positive test. Table  1 summarises the variables included in each model to predict the three conditional probabilities at the three timepoints. When issues with variables perfectly predicting the outcome were encountered, relevant variables were dropped. This only happened at the 3-month time-point. The concordance statistic ( C) was used to assess the predictive performance of the models: values 0.7 and 0.8 denoting good and strong performance, respectively, with a value of ≤ 0.5 indicating poor prediction [Table  1 ; 29 , 30 ].

At each time-point, the envisioned population weight was calculated as the product of the three corresponding ‘mini’ survey weights. Taking the example of 3 months post-testing: to re-weight from the previously used analytic sample to the envisioned CLoCk population, the fourth created survey weight comprised the product of the following three survey weights: Response 3 months , Timely response 3 months , and (Re)infection 3 months (Fig.  3 ). The four survey weights at each time point (twelve in total) are flexible and can be combined as required, to create final survey weights to get to the target population as described in the illustrative example.

Weighting to the general population

Generated survey weights re-weight the analytic sample to the CLoCk envisioned population, that is, CYP invited to participate if they had a PCR-test within the pre-specified timepoints. However, as PCR testing varied by region and stage of the pandemic [ 31 , 32 ], the envisioned population may not be fully representative of the general population of CYP in England. This is because, for example, not all CYP in England will have been able to access/complete a PCR-test. Hence, final survey weights used to get to the required target population were re-calibrated to the general population, using data on sex, age, and region from the 2021 UK Census [ 33 ]. To do this, ratios of the Census data to CLoCk data reweighted to the target population of interest were produced (see Additional File 2 for the interactive tool used to calculate these) with the final target population survey weights then multiplied by these ratios. See Additional File 2 for how this was done for the illustrative example below.

Weight trimming

All survey weights (i.e., each of the response given envisioned to take part, timely response given response, (re)infection given timely response, and the ‘envisioned population’ survey weights) were trimmed to reduce the likelihood of extremely large survey weights increasing variance [ 34 ]. This was done by reducing extreme survey weights to a cut-off defined as the median +  k × interquartile range. k is typically either 3 or 4 [ 35 ]. In the present study we took a conservative approach and set k as 3. All survey weights were multiplied by a factor to re-calibrate back up to the original sum of weights [ 36 ]. When combining survey weights for the illustrative example below, untrimmed survey weights were initially used with the final survey weights trimmed.

Illustrative example: replicating published findings

Findings from CLoCk show the overall prevalence of tiredness and shortness of breath are high in CYP at baseline (i.e., at the time of their index PCR test) and increase over time to 12 months [ 15 ]. Here we compare the prevalence of tiredness and shortness of breath over a 12-month period from a previous publication [ 15 ] to prevalences that were weighted to the (i) target, and (ii) general populations. We demonstrate how uncertainty around generated weights can be accounted for via bootstrapping (with 1000 replications) and supply illustrative code for this (Additional File 1 : Text 1). To be included in the published analytic sample ( n  = 5,085), CYP first registering in January-March 2021 must have completed their 3-month questionnaire (to provide information about their symptoms at the time of their PCR-test, i.e., at baseline), and be in the analytic sample at 6- and 12-months. For those registering in October-December 2020, they must meet the requirements to be included in the analytic samples at both 6- and 12-months (see Fig.  1 for cohort breakdown and Fig.  4 for participant flow for this example). Therefore, longitudinal weights were created by combining the survey weights as detailed in Fig.  5 and further illustrated in the bootstrap example in Text 1 (Additional File 1 ).

Steps taken to combine survey weights to replicate published CLoCk findings [ 15 ]

Note . To be included in the analytic sample, children and young people must have provided information about their symptoms at the time of their PCR test (i.e., 0 months). This information is gathered at study enrolment meaning criteria for inclusion varied depending on month of index PCR-test. Children and young people with an index test in January, February and March 2021 must have responded to the 3-month questionnaire (to gather information about baseline symptoms) as well as meet the criteria for inclusion in the analytic samples at 6- and 12-months post-testing (i.e., responded, done so timely and not (re)infected). Children and young people with an index-test in October, November, and December 2020 only had to meet the criteria for inclusion in the analytic samples at 6- and 12-months

At the 3-month sweep, 7,135 CYP were included in the analytic sample, constituting 14% of the envisioned population at that time-point ( N  = 50,845, Table  2 ; Fig.  2 ). The analytic sample at 6 months ( n  = 12,946) comprised 10% of the envisioned population ( N  = 127,894); at 12-months, 15,624 were included in the analytic sample, forming 7% of the 12-month envisioned population ( N  = 219,175). Overall, 31,012 CYP completed at least one questionnaire, with 42,264 questionnaires completed. CYP in the analytic samples at 3-, 6-, and 12-months completed the questionnaire at a median of 14.9 (IQR: 13.1–18.9), 27.9 (IQR: 26.3–29.7), and 52.7 (IQR: 51.3–54.9) weeks post-testing, respectively. Compared to the envisioned population, CYP in the analytic samples were older, female and from less deprived areas (Table  2 ).

The C statistics for all required conditional probabilities varied between 0.60 (responding timely given responded at 12 months) to 0.77 ((re)infected given timely response at 12-months and 6-months, see Table  1 ). Table  3 displays the survey weights generated for each data collection sweep along with the relevant Ns , medians, and interquartile ranges.

Re-weighting published findings

Consistent with published findings [ 15 ], the overall prevalence of tiredness and shortness of breath increased from baseline to 12-months post-index PCR-test in both test-positive and test-negative CYP even after weighting (and trimming) to the target and general populations (Tables  4 and 5 ; Figs.  6 and 7 ). For example, at time of testing, the unweighted overall prevalence of tiredness in CYP who tested negative for SARS-CoV-2 was 3.63%. When weighted (and trimmed) to the target population the prevalence was 3.51% and when weighted (and trimmed) to the general population the prevalence was 3.69% (Table  4 ). Likewise, prevalences of tiredness and shortness of breath by time of first report remained similar to published findings (Figs.  6 and 7 ). Results using trimmed and untrimmed weights were broadly similar (Additional File 1 : Tables 2 and 3 ; Figs.  1 and 2 ). Table  4 (Additional File 1 ) shows the uncertainty around the generated target population weight (untrimmed); results are broadly consistent.

Weighted (trimmed) and unweighted tiredness prevalences 0-12-months post-index PCR-test by time of first report

Weighted (trimmed) and unweighted shortness of breath prevalences 0-12-months post-index-PCR-test by time of first report

The present study aimed to (i) create weights for the CLoCk study at its data collection sweeps 3-, 6- and 12-months post-index PCR-test, and (ii) apply the developed survey weights to the analysis of shortness of breath and tiredness over the 12-month period to determine whether accounting for any biases in the target population, response, attrition or (re)infection affected published results. Flexible survey weights for the CLoCk study were developed and applied in an illustrative example. When applying the survey weights, results were consistent with published CLoCk findings [ 15 ]. That is, the overall prevalence of tiredness and shortness of breath increased over time from baseline to 12-months post-testing in both test-positive and test-negative CYP.

A major strength of the present study includes the flexibility of the survey weights developed whereby the creation of separate ‘mini’ survey weights (i.e., response, timely response and (re)infection) and the overall ‘envisioned population’ weight ensures researchers are able to combine them to re-create their specific target population, which will vary depending on the specific research question being asked. The interactive tool provided will allow researchers to re-calibrate their target population weights to the general population of CYP in England using the recent Census 2021 data. This re-calibration attempts to address the potential bias in the envisioned CLoCk population due to variation in PCR testing by region and stage of the pandemic [ 31 , 32 ]. Furthermore, by trimming survey weights using a technique that is unaffected by the size of the largest survey weight [ 34 ], we improve the accuracy and precision of final parameter estimates in re-weighted analyses [ 37 ]. Moreover, we used a range of data from both the UKHSA dataset and the CLoCk questionnaire to develop the models that predicted the required conditional probabilities. We acknowledge that the C statistics, particularly for models used to predict the probability of responding given envisioned to take part and the probability of responding timely given responded were somewhat low ranging between 0.60 and 0.73. However, for the probability of responding given envisioned to take part, it should be noted that the C statistic cannot be further improved due to the lack of additional data relating to the envisioned CLoCk population (here, only data held on the UKHSA database for matching was available). Thus, for all survey weight generation, but here in particular, one should note the constraint deriving from the variables used to generate conditional probabilities and the potential for the non-response/attrition/selection mechanisms to be dependent on unmeasured variables. For example, it might be that those with severe tiredness are less likely to respond. Relatedly, our approach is appropriate when missingness is assumed to be dependent on observed characteristics, but as mentioned above this may not be the case. This is an important potential limitation, with the implication being survey weights do not fully adjust for such (non-response, attrition, and sample selection) bias, though we attempt to minimise its impact. In an attempt to avoid potential recall bias, for the latter two ‘mini’ weights, we made the pragmatic decision to only consider questionnaire data asked in relation to health and wellbeing at the time of questionnaire completion.

We acknowledge concerns regarding the use of stepwise selection processes whereby inclusion of too many candidate variables may result in nuisance variables being selected over true variables meaning the best model is not provided [ 38 ]. We were mindful of this when selecting the initial list of potential predictors, determined the best functional forms of continuous variables used in all regressions, and used theoretical arguments to inform our selection, as recommended [ 39 ]. Finally, it should be noted that the survey weights are estimated and if treated as observed there is a risk of overestimating the precision of the estimates. To address this, we provide an example of how variabilities due to generating the weights can be accounted for via bootstrapping.

CLoCk is the largest known prospective study of Long Covid in non-hospitalised CYP, with over 30,000 respondents. Like all longitudinal population-based studies, issues regarding selection into the study and attrition over time need to be considered. The present findings suggest the CLoCk sample is representative of the envisioned and general populations of CYP in England, although the developed weights need to be utilised in multiple and different contexts to assess their impact and identify whether current conclusions are consistent across other CLoCk analyses. The same approach can and should be taken in other research studies to assess sample representativeness. Importantly, application of survey weights more generally is beneficial as a way of addressing the impact of potential bias.

Availability of data and materials

Data are not publicly available. All requests for data will be reviewed by the Children & young people with Long Covid (CLoCk) study team, to verify whether the request is subject to any intellectual property or confidentiality obligations. Requests for access to the participant-level data from this study can be submitted via email to the corresponding author with detailed proposals for approval. A signed data access agreement with the CLoCk team is required before accessing shared data.

Abbreviations

United Kingdom Health Security Agency

United Kingdom

Children and Young People

Children and young people with Long Covid

Strengths and Difficulties Questionnaire

EuroQol Visual Analogue Scale

Index of Multiple Deprivation

Chalder Fatigue Scale

Short Warwick Edinburgh Mental Wellbeing Scale

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Acknowledgements

Michael Lattimore, UKHSA, as Project Officer for the CLoCk study.

Olivia Swann and Elizabeth Whittaker designed the elements of the ISARIC Paediatric COVID-19 follow-up questionnaire which were incorporated into the online questionnaire used in this study to which all the CLoCk Consortium members contributed.

This work is independent research jointly funded by the National Institute for Health and Care Research (NIHR) and UK Research & Innovation (UKRI) who have awarded funding grant number COVLT0022. SMPP is supported by a UK Medical Research Council Career Development Award (MR/P020372/1). DH is supported by the NIHR through the Applied Research Collaboration (ARC) North-West London and the School of Public Health Research. All research at Great Ormond Street Hospital Charity NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, UKRI or the Department of Health and Social Care.

Additional members of the CLoCk Consortium.

Trudie Chalder, King’s College London, [email protected].

Tamsin Ford, University of Cambridge, [email protected].

Isobel Heyman, University College London, [email protected].

Shamez Ladhani, St. George’s University of London and UK Health Security Agency, [email protected].

Marta Buszewicz, University College London, [email protected].

Esther Crawley, University of Bristol, [email protected].

Bianca De Stavola, University College London, [email protected].

Shruti Garg, University of Manchester, [email protected].

Anthony Harnden, Oxford University, [email protected].

Michael Levin, Imperial College London, [email protected].

Vanessa Poustie, University of Liverpool, [email protected].

Kishan Sharma, Manchester University NHS Foundation Trust (sadly deceased).

Olivia Swann, Edinburgh University, [email protected].

This work is independent research jointly funded by the National Institute for Health and Care Research (NIHR) and UK Research & Innovation (UKRI) who have awarded funding grant number COVLT0022. SMPP is supported by a UK Medical Research Council Career Development Award (MR/P020372/1). DH is supported by the NIHR through the Applied Research Collaboration (ARC) North-West London and the School of Public Health Research. All research at Great Ormond Street Hospital NHS Foundation Trust and UCL Great Ormond Street Institute of Child Health is made possible by the NIHR Great Ormond Street Hospital Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Natalia K Rojas, Tom Norris & Snehal M Pinto Pereira

UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK

Bianca L De Stavola, Mario Cortina-Borja, Manjula D Nugawela, Emma Dalrymple, Terence Stephenson & Roz Shafran

Mohn Centre for Children’s Health & Wellbeing, School of Public Health, Imperial College London, London, UK

Dougal Hargreaves

Immunisation Department, Health Security Agency, 61 Colindale Avenue, London, NW9 5EQ, UK

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Contributions.

Natalia K Rojas [email protected] conducted the statistical analysis for the manuscript, accessed and verified the data and drafted the manuscript. Bianca De Stavola [email protected] assisted in the design of the statistical analyses and reviewed the manuscript. Tom Norris [email protected] assisted the statistical analysis including its design and contributed to the drafting of the manuscript. Mario Cortina-Borja [email protected] reviewed the manuscript. Manjula D Nugawela [email protected] assisted the statistical analysis for the manuscript, accessed and verified the data and reviewed the manuscript. Dougal Hargreaves [email protected] reviewed the manuscript. Emma Dalrymple [email protected] contributed to the design of the CLoCk study and reviewed the manuscript. Kelsey McOwat [email protected] adapted the questionnaire for the online SNAP survey platform. Ruth Simmons [email protected] accessed and verified the data, designed the participant sampling and dataflow for the CLoCk study. Terence Stephenson [email protected] conceived the idea for the CLoCk study, submitted the successful grant application and reviewed the manuscript. Roz Shafran [email protected] contributed to the design of the CLoCk study, submitted the ethics and R&D applications and reviewed the manuscript. Snehal M Pinto Pereira [email protected] conceived the idea for the present study, designed and assisted the statistical analyses for the manuscript, accessed and verified the data and drafted the manuscript. All members of the CLoCk Consortium made contributions to the conception or design of the work; were involved in drafting both the funding application and this manuscript; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Natalia K Rojas .

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Competing interests.

Terence Stephenson is Chair of the Health Research Authority and therefore recused himself from the Research Ethics Application. Dougal Hargreaves had a part-time secondment as Deputy Chief Scientific Adviser from September 2020 to September 2021, whereby his salary for 2 days per week was paid by the Department for Education (England) to Imperial College London. All remaining authors have no conflicts of interest.

Ethics approval and consent to participate

Ethical approval was provided by the Yorkshire & The Humber - South Yorkshire Research Ethics Committee (REC reference: 21/YH/0060; IRAS project ID:293495). Public Health England (now UKHSA) has legal permission, provided by Regulation 3 of The Health Service (Control of Patient Information) Regulations 2002, to process patient confidential information for national surveillance of communicable diseases. Parents/carers were sent an invitation by post sent through PHE/UKHSA on behalf of the research team with a link to the website with the relevant information sheets and consent forms and they had the opportunity to ask any questions about the study. Parents/carers of CYP under 16 years of age were asked to complete an online parent/carer consent form. The young person was also asked to complete an online assent form to indicate their agreement. Consent was asked online from 16–17-year-olds (using the Young Person Consent Form) in line with Health Research Authority recommended processes. Informed consent was obtained from all participants and/or their legal guardian. All experiments were performed in accordance with relevant guidelines and regulations (such as the Declaration of Helsinki).

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12874_2024_2219_MOESM1_ESM.docx

Additional file 1. Additional Tables, Text and Figures. This file contains additional Tables 1, 2, 3 and 4, Text 1 and Figs. 1 and 2. Table 1. Further information on variables included in stepwise selection processes for weight generation and their handling. Text 1. Illustrative code demonstrating how uncertainty around generated weights can be accounted for via bootstrapping (with 1000 replications). Table 2. Tiredness prevalence 0 to 12-months post-index PCR-test weighted (trimmed and untrimmed) and unweighted. Table 3. Shortness of breath prevalence 0 to 12-months post-index PCR-test weighted (trimmed and untrimmed) and unweighted. Table 4. Illustrative example of tiredness prevalence 0 to 12-months post-index PCR-test weighted to the target population (untrimmed) with bootstrapped confidence intervals (1000 replications). Figure 1. Weighted (trimmed and untrimmed) and unweighted tiredness prevalences by time of first report. Figure 2. Weighted (trimmed and untrimmed) and unweighted shortness of breath prevalences by time of first report.

12874_2024_2219_MOESM2_ESM.xlsx

Additional file 2: Interactive online tool for re-calibration of survey weights to the general population. This can be used to re-calibrate final target population survey weights to the general population using data on sex, age, and region from the 2021 UK Census. The tool allows ratios of the Census data to CLoCk data reweighted to the target population to be produced and provides examples of what to do with these ratios.

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Rojas, N.K., De Stavola, B.L., Norris, T. et al. Developing survey weights to ensure representativeness in a national, matched cohort study: results from the children and young people with Long Covid (CLoCk) study. BMC Med Res Methodol 24 , 134 (2024). https://doi.org/10.1186/s12874-024-02219-0

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Effects of time of the day at sampling on CSF and plasma levels of Alzheimer’ disease biomarkers

• Anna Orduña Dolado 1 ,
• Erik Stomrud 1 , 2 ,
• Nicholas J. Ashton 3 , 4 , 5 , 6 ,
• Johanna Nilsson 3 , 7 ,
• Clara Quijano-Rubio 8 ,
• Alexander Jethwa 9 ,
• Wagner S. Brum 3 , 10 ,
• Ann Brinkmalm Westman 3 , 7 ,
• Henrik Zetterberg 3 , 7 , 11 , 12 , 13 , 14 ,
• Kaj Blennow 3 , 7 , 15 , 16 ,
• Shorena Janelidze 1 &
• Oskar Hansson 1 , 2  

Alzheimer's Research & Therapy volume  16 , Article number:  132 ( 2024 ) Cite this article

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Studies suggest that cerebrospinal fluid (CSF) levels of amyloid-β (Aβ)42 and Aβ40 present a circadian rhythm. However sustained sampling of large volumes of CSF with indwelling intrathecal catheters used in most of these studies might have affected CSF dynamics and thereby confounded the observed fluctuations in the biomarker levels.

We included 38 individuals with either normal (N = 20) or abnormal (N = 18) CSF Aβ42/Aβ40 levels at baseline. CSF and plasma were collected at two visits separated by an average of 53 days with lumbar punctures and venipunctures performed either in the morning or evening. At the first visit, sample collection was performed in the morning for 17 participants and the order was reversed for the remaining 21 participants. CSF and plasma samples were analyzed for Alzheimer’ disease (AD) biomarkers, including Aβ42, Aβ40, GFAP, NfL p-tau181, p-tau217, p-tau231 and t-tau. CSF samples were also tested using mass spectrometry for 22 synaptic and endo-lysosomal proteins.

CSF Aβ42 (mean difference [MD], 0.21 ng/mL; p  = 0.038), CSF Aβ40 (MD, 1.85 ng/mL; p  < 0.001), plasma Aβ42 (MD, 1.65 pg/mL; p  = 0.002) and plasma Aβ40 (MD, 0.01 ng/mL, p  = 0.002) were increased by 4.2-17.0% in evening compared with morning samples. Further, CSF levels of 14 synaptic and endo-lysosomal proteins, including neurogranin and neuronal pentraxin-1, were increased by 4.5-13.3% in the evening samples (MD range , 0.02-0.56 fmol/µl; p  < 0.042). However, no significant differences were found between morning and evening levels for the Aβ42/Aβ40 ratio, different p-tau variants, GFAP and NfL. There were no significant interaction between sampling time and Aβ status for any of the biomarkers, except that CSF t-tau was increased (by 5.74%) in the evening samples compared to the morning samples in Aβ-positive (MD, 16.46 ng/ml; p  = 0.009) but not Aβ-negative participants (MD, 1.89 ng/ml; p  = 0.47). There were no significant interactions between sampling time and order in which samples were obtained.

Our findings provide evidence for diurnal fluctuations in Aβ peptide levels, both in CSF and plasma, while CSF and plasma p-tau, GFAP and NfL were unaffected. Importantly, Aβ42/Aβ40 ratio remained unaltered, suggesting that it is more suitable for implementation in clinical workup than individual Aβ peptides. Additionally, we show that CSF levels of many synaptic and endo-lysosomal proteins presented a diurnal rhythm, implying a build-up of neuronal activity markers during the day. These results will guide the development of unified sample collection procedures to avoid effects of diurnal variation for future implementation of AD biomarkers in clinical practice and drug trials.

Introduction

There is a great need for fluid biomarkers that robustly reflect various aspects of the pathophysiology of Alzheimer’s disease (AD) to improve the diagnostic workup, monitor progression and enable effective drug-development. Currently available fluid biomarkers include amyloid-β (Aβ)42, alone or in ratio with Aβ40 and phosphorylated tau (e.g., p-tau181, p-tau217 and p-tau231) reflecting core AD-related Aβ and tau pathologies, respectively. Additional promising biomarkers of pathophysiological processes that are common for many neurodegenerative disorders are neurofilament light (NfL), a marker of axonal degeneration, as well as a marker of glial activation, glial fibrillary acidic protein (GFAP) [ 1 ]. Although cerebrospinal fluid (CSF) measures are available and have proven highly useful as diagnostic and prognostic tools for AD in research settings, clinical care and drug trials, blood testing offers several advantages (e.g., lower invasiveness, higher accessibility, cost-effectiveness) [ 2 ]. Accumulating evidence suggests that plasma Aβ42/40, different p-tau isoforms (p-tau181, p-tau231 and p-tau217), NfL and GFAP approach in performance [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ] or even outperform [ 10 ] their corresponding CSF biomarker.

To improve biomarker performance in clinical care and trials, it is important to implement standardized sample collection and handling procedures that would minimize the effects of pre-analytical component among factors impacting biomarker variability [ 11 , 12 , 13 , 14 ]. One such pre-analytical factor to consider is time of the day at sample collection. Even though, published protocols for CSF sampling recommend to perform lumbar puncture (LP) at a standardized time (08.00–12.00 AM) to avoid potential diurnal variation for CSF biomarkers [ 15 ], diurnal variability in CSF and plasma concentrations of AD biomarkers is not well established. Early reports showed fluctuations of CSF Aβ with 1.6-to-4-fold change over a 36-hour period in younger non-demented participants with good general health [ 16 ]. Other studies have found smaller (5.5 to 6.7%) or no significant fluctuation in older, more clinically relevant cohorts [ 17 , 18 ]. Two studies have investigated Aβ dynamics in plasma, reporting 5–9% higher levels of Aβ42 and Aβ40 in samples collected in the afternoon versus morning and larger diurnal fluctuations in younger individuals than in older individuals [ 19 , 20 ]. While data on NfL and GFAP, either in CSF or plasma are lacking, some evidence suggest that CSF t-tau or p-tau levels do not follow diurnal pattern both in healthy old population [ 17 ] and in neurosurgical patients with CSF pressure monitoring [ 21 ].

Synaptic homeostasis alteration and degeneration are early pathological events common in many neurodegenerative diseases, including AD. This makes synaptic proteins that reflect synaptic dysfunction interesting early biomarkers [ 22 ]. Disruption of sleep and circadian rhythm is believed to happen with ageing and contribute to development of neurodegenerative diseases in part through synaptic dysfunction [ 23 ]. AD as well as other proteinopathies are also accompanied by aberrant function of endo-lysosomal networks [ 24 ]. Several articles reported increased levels of endo-lysosomal proteins in CSF of patients with AD while their levels seem to decrease in Parkinson’s disease [ 25 , 26 , 27 , 28 ]. To the best of our knowledge, no studies have assessed variations in synaptic or endo-lysosomal protein levels during the day in humans.

Most studies on changes in CSF Aβ levels to date used frequent and sustained sampling throughout the day with an indwelling intrathecal catheter. This procedure has been shown to contribute to the rise in CSF Aβ42 and Aβ40 independent of circadian fluctuations as repeated lumbar sampling presumably drives the redistribution of CSF flow towards the lumbar space where it is collected [ 19 , 29 , 30 ]. To minimize the effect of sampling procedures on CSF biomarker concentrations, participants in the present study underwent two LPs, one in the morning and another one in the evening, separated by an average of 53 days and samples were analyzed for all major AD biomarkers as well as a panel of 22 synaptic and endo-lysosomal proteins. To ensure that changes in biomarker levels were not due to the sampling order, 17 participants had the first visit in the morning and the second in the evening and the order was reversed for the remaining 21 participants. In addition to CSF, we collected plasma samples on the same visit and quantified the most promising plasma AD biomarkers using currently best performing immunoassays [ 31 , 32 ]. Our primary research question was whether the time at sample collection, morning or evening, affected the levels of different biomarkers (Aβ42, Aβ40, Aβ42/40, NfL, GFAP, p-tau217, p-tau181, p-tau231, t-tau and synaptic and endo-lysosomal proteins). A secondary research question was whether any of these differences were affected by the amyloid status of the participants.

Participants

Participants were enrolled at the Memory Clinic, Skåne University Hospital comprising clinical patients who underwent LP as a component of their clinical assessment, along with individuals from the longitudinal Swedish BioFINDER study. The inclusion of the participants from the BioFINDER study contributed to the relatively high numbers of asymptomatic subjects with unimpaired cognition. Participants were selected such that the numbers of Aβ + and Aβ- individuals were approximately the same. The sole inclusion criterion for participation in this study was the performance of a LP at the clinic. Exclusion criteria consisted of individuals who did not undergo the requisite two LP. All participants had two visits when LPs and venipunctures were performed approximately at the same time. We believe that any damage and CSF leakage caused by LP at the first visit would have healed after approximately one month. Therefore, study participants had second visit with LP and venipuncture on an average 53 days (range 41–65 days) after the first visit. For 17 participants the first collection was performed in the morning and the following in the evening and for the remaining 21 participants the order was reversed. Time difference between morning and evening samplings was on an average 10:30 h (range 9:45 − 11:45 h).

Plasma and CSF collection and analysis

20mL of CSF was collected in 5-mL LoBind tubes. CSF was centrifuged (2000 g, + 4 °C) for 10 min, aliquoted in 1.5 mL polypropylene tubes and stored at − 80 °C within 30–60 min of collection [ 15 ]. Blood was collected in EDTA-plasma tubes (Vacutainer K2EDTA tube, BD Diagnostics) and centrifuged (2000 g, + 4 °C) for 10 min. Resulting plasma was transferred into one 50-mL polypropylene tube, mixed and aliquoted into 1.5 mL polypropylene tubes and stored at − 80 °C within 30–60 min of collection. All samples from the same patient were measured in the same run to limit the effects of run-to-run variability on biomarker concentrations.

CSF levels of Aβ40, t-tau, NfL and GFAP were measured as part of robust prototype assay within the NeuroToolKit, on fully automated cobas® e 411 or e 601 analyzers (all Roche Diagnostics International Ltd, Rotkreuz, Switzerland) as previously described [ 33 ]. CSF Aβ42 levels were measured as part of the Roche NeuroToolKit using the in vitro diagnostic (IVD) Elecsys® assay [ 34 ]. Plasma levels of Aβ42, Aβ40, GFAP and NfL were also measured as part of the Roche NeuroToolKit using Elecsys® plasma prototype immunoassays (All Roche Diagnostics International Ltd, Rotkreuz, Switzerland) on cobas® e 411 and cobas e 601 instruments as previously described [ 33 ]. CSF and plasma p-tau231 and p-tau181 levels were measured by an in-house Simoa assay developed in the University of Gothenburg, as previously described [ 35 , 36 ]. CSF and plasma p-tau-217 levels were measured using an immunoassay developed by Lilly Research laboratories on the Meso-Scale Discovery Platform as previously described [ 4 ].

CSF samples were analyzed for a panel of 18 synaptic proteins and 4 endo-lysosomal proteins (See Supplementary Table 1 , Supplemental 1 ) using liquid chromatography with tandem mass spectrometric analysis (LC–MS/MS) as previously described [ 37 ].

Study participants were classified as amyloid negative (Aβ-) or positive (Aβ+) using CSF Aβ42/Aβ40 quantified with the Food and Drug administration (FDA)-approved Lumipulse G assay and established cut-off of 0.072 [ 38 ].

Statistical analyses

Differences in the demographic variables were evaluated with Student t-test (age, Mini Mental State Examination (MMSE) scores, estimated glomerular filtration rate (eGFR, as an indicator of kidney dysfunction) and Body Mass Index (BMI)) or Fisher’s exact test (gender, APOE ε4 carriership and diagnosis). Repeated measures two-way ANOVA including interaction effect between Aβ status and time at sampling was used to assess whether biomarker levels in Aβ + and Aβ- individuals were affected differently by time of sample collection. Similar analysis was carried out to assess interaction between order in which samples were collected (i.e., morning collection or evening collection first) and time at sampling. Multiplicity correction was applied using the Bonferroni-Dunn method except the CSF synaptic and endo-lysosomal panel where we used Benjamini–Hochberg false discovery rate (FDR). All significance were two-sided with significance level equal to 0.05. Statistical analysis was performed using Prism 9 (GraphPad Software, San Diego, California, USA).

Participant demographics

The demographic and clinical data for all participants are summarized in Table  1 . Out of 38 participants, 18 were Aβ-positive (Aβ+) and 20 A β-negative (Aβ-). There were no significant differences between Aβ + and Aβ- groups for sex, age, MMSE score, diagnosis, eGFR or BMI. There was a higher proportion of APOE ε4 carriership in Aβ + in comparison with Aβ- (70.6% vs. 15.0%, p  = 0.002). Most study participants (35 out of 38) were cognitively unimpaired while 3 individuals were cognitively impaired.

CSF and plasma AD biomarkers

CSF concentrations of Aβ42 (mean difference [MD], 0.21 ng/mL; p  = 0.038) and Aβ40 (MD, 1.85 ng/mL; p  < 0.001) were increased by 17.0% (95% CI, 10-24.1) and 10.5% (95% CI, 6.5–14.4), respectively, in samples collected in the evening compared to those collected in the morning (Fig.  1 ; Table  2 ). Similarly, plasma levels of Aβ42 (MD, 1.65 pg/mL; p  = 0.002) and Aβ40 (MD, 0.01 ng/mL, p  = 0.002) were significantly higher in samples collected in the evening compared to those collected in the morning. However, the increases were smaller for plasma Aβ42 (4.8%; 95% CI, 2.7–6.7) and Aβ40 (4.2%; 95% CI, 2.2–6.3) compared with CSF Aβ42 and Aβ40 (Fig.  2 ; Table  3 ). In contrast, we did not find any significant differences in either the CSF Aβ42/Aβ40 ratio (5.3%; 95% CI, 1.6- 9.0; p  = 0.16) or the plasma Aβ42/Aβ40 ratio (0.7%; 95% CI, -0.7 to 1.1; p  = 1.0) (Figs.  1 and 2 ; Tables  2 and 3 ). We also did not find any changes in CSF and plasma levels of p-tau217, p-tau181, p-tau231, NfL and GFAP ( p  = 1.00) between collection in the morning and evening (Figs.  1 and 2 ; Tables  2 and 3 ).

CSF biomarkers levels in samples collected in the morning and evening . Subject specific biomarker concentration in samples collected in the morning vs. evening. Average percent changes between time points are shown in box-plots plotted with the Tukey method. Blue and orange dots represent participants with negative and positive amyloid status, respectively. Asterisks represent p -values for the main effects of sampling time from repeated measures two-way ANOVA; * p  < 0.05, *** p  < 0.001

Plasma biomarkers levels in samples collected in the morning and evening. Subject specific biomarker concentration in samples collected in the morning vs. evening. Average percent changes between time points are shown in box-plots plotted with the Tukey method. Blue and orange dots represent participants with negative and positive amyloid status, respectively. Asterisks represent p -values for the main effects of sampling time from repeated measures two-way ANOVA; ** p  < 0.01

Effects of sampling time on CSF and plasma biomarkers were not different in the Aβ+ and Aβ- groups (p range uncorrected 0.06–0.88 for interaction between sampling time and Aβ status, Supplementary Table 3 , Supplementary Figs.  2 – 3 ), except CSF t-tau ( p  = 0.007). There was a relatively small increase (5.74% (95% CI, 2.85–8.63)) in CSF t-tau levels in evening samples compared to the morning samples in Aβ+ participants (MD, 16.46 ng/ml; p  = 0.009) but not in Aβ- participants (1.20% (95%CI, -1.99-4.39); MD, 1.89 ng/ml; p  = 0.47) (Supplementary Fig.  2 ).

Morning or evening biomarker levels were not different depending on the order in which samples were collected (morning first vs. evening first) for any of the biomarkers (p range uncorrected 0.051–0.85 for interaction between sampling time and order of sample collection, Supplementary Table 3 ).

Synaptic and endo-lysosomal panel

We found differences between morning and evening samples for 14 out of 22 synaptic and endo-lysosomal proteins (Table  4 and Supplementary Table 2 , Supplemental 1 ). CSF levels of amyloid precursor protein (APP), syntaxin-1B (STX1B), neurogranin (Ng), neuronal pentraxin receptor (NPTXR), neuronal pentraxin 1 (NPTX1), β-synuclein (β-Syn), γ-synuclein (γ-Syn), 14-3-3ε, phosphatidylethanolamine-binding protein 1 (PEBP-1), cathepsin F (CTSF), GM2 activator (GM2A), neurosecretory protein VGF (VGF), secretogranin-2 (SgII) and chromogranin A (CgA) were all increased by 4.5-13.3% (95% CI, 1.7–7.2 to 4.4–22.1; p  < 0.048) in samples collected in the evening compared to those collected in the morning (Supplementary Fig.  1 , Supplemental 1 , Table  4 ).

Effects of sampling time were not different in the Aβ + and Aβ- groups (p range uncorrected 0.06–0.88, for interaction between sampling time and Aβ status) for any of the proteins from the MS panel. In addition, morning or evening protein levels were not different depending on the order in which samples were collected (morning first vs. evening first) (p range uncorrected 0.09–0.85, for interaction between sampling time and order of sample collection).

In this study, we show higher levels of Aβ42 and Aβ40 in samples collected in the evening compared to those collected in the morning. The increases were modest and consistent in both CSF and plasma. Importantly, no changes were observed in the Aβ42/Aβ40 ratio, or any other tested AD biomarker (i.e., p-tau217, p-tau231, p-tau181, NfL and GFAP) either in CSF or plasma. Additionally, 14 out of 22 synaptic and endo-lysosomal proteins were also increased in CSF in the evening in comparison to the morning samples.

Although there have been handful of studies on diurnal variation in the CSF levels of AD biomarkers, results have been inconsistent. Some have pointed to fluctuations in biomarker concentrations during the day [ 16 , 17 , 30 ], whereas other have not found any significant changes [ 18 , 21 ]. The conflicting results are possibly caused by the small sample size in several of the studies, cohort specific differences as well as differences in the CSF sampling methods and assays used for Aβ quantification. Many reports have highlighted that frequent sampling and extraction of large volumes of CSF via indwelling catheter leads to increased levels of CSF Aβ [ 17 , 19 , 29 , 39 , 40 , 41 ] possibly by promoting the transfer from the interstitial fluid to CSF [ 17 ] and by redistribution of fluid towards the lumbar space [ 29 , 41 ]. To mitigate these sampling-related effect, CSF in the present study was collected at two separate LPs with an average interval of 53 days allowing sufficient time for tissue damage caused by the LP to heal. Of note, no interaction effects were seen between sampling time (i.e., morning and evening) and order in which samples were collected for any biomarker indicating that the differences in levels we report were not due to samples being collected at a later date. Our results support those that suggest a circadian rhythm for Aβ42 and Aβ40, with higher levels in the evening. The increases in CSF were modest with 17% and 10.5% for Aβ42 and Aβ40, respectively, (Table  2 ) and in a similar range (3.8–15%) to some studies that have included elderly subjects as well as patients with AD [ 17 , 29 ]. The increases in plasma Aβ42 and Aβ40 were lower than in CSF (4.8% and 4.2% respectively; Table  3 ) and in a similar range (2-9%) as in previous reports [ 19 , 20 ]. The smaller changes in the evening of Aβ in plasma could be partly due to the contribution of peripheral sources of Aβ that are less affected by circadian rhythms. The differences in Aβ42 and Aβ40 levels in the morning and evening samples were not influenced by brain Aβ status, which is important since 47% of our sample had abnormal Aβ-status (Table  1 ). These findings are in line with previous reports indicating that in elderly individuals day/night variability in Aβ42 and Aβ40 levels, did not vary between Aβ + and Aβ- groups [ 19 , 39 ]. At the same time, we show that CSF and plasma Aβ42/Aβ40 ratios remained unaltered, suggesting that increased production or decreased clearance of Aβ peptides during daytime similarly affect the CSF and plasma levels of the Aβ42 and Aβ40.

Our results with higher APP levels in the evening in comparison to the morning suggest that circadian rhythm and synaptic activity might affect brain and CSF Aβ levels through modulation of APP expression, release and/or metabolism (Table  4 ). Aβ42 and Aβ40 are produced by the cleavage of APP and increased synaptic activity promotes the amyloidogenic processing of APP [ 42 ] leading to increased interstitial Aβ levels [ 43 ]. Interestingly, it has been shown in mice that interstitial fluid levels of Aβ correlate with time spent awake and change in response to activation of orexin which is known to regulate wakefulness under physiological conditions and follow a diurnal fluctuation [ 44 , 45 ].

In agreement with earlier data, we did not find any significant fluctuations over the day for CSF or plasma p-tau [ 18 , 21 ]. Furthermore, we show for the first time, that there are no differences in CSF and plasma NfL and GFAP concentrations between samples collected in the morning and evening. Collectively, these results suggest that during daytime there is a specific increase in the CSF and plasma levels of Aβ proteins rather than a general build-up of AD biomarkers.

Higher neuronal activity and increased synaptic strength during wakefulness compared to sleep have been reported in mice and rats [ 46 , 47 , 48 ]. High synaptic activity is associated with increased production of synaptic proteins, especially proteins that regulate the secretory pathways [ 49 ]. Taken together these findings may explain the higher levels of synaptic proteins in evening samples in comparison to the morning seen in our study (See Supplementary Table 2 , Supplemental 1 , Table  4 ). It remains unclear why only some synaptic and endo-lysosomal proteins were selectively affected in our study. Future investigations should explore the underlying mechanisms behind these findings.

The strength of the current study is that we assessed a wide range of the established and candidate CSF and plasma AD biomarkers measured using state-of-the art assays. However, this study has limitations. The sample size was relatively small and determined based on previous studies examining the effects of diurnal variability on Aβ levels (no power calculations were performed). The difference in biomarker levels between the morning and evening samples, in plasma in particular, were also small with intra-individual variability potentially influencing these results. Future work in larger cohorts accounting for the effects of intra-individual variability in biomarker concentration are warranted. These studies should also assess the impact of diurnal variability on diagnostic performance of AD biomarkers.

Conclusions

In summary, we demonstrate that Aβ42 and Aβ40 levels in CSF and plasma have diurnal fluctuations with higher levels in the evening. Previous data have indicated that Aβ42/Aβ40 ratio is less affected than Aβ42 alone by different AD non-specific factors and pathologies (e.g., pre-analytical sample handling, inter-individual variability in Aβ levels, subcortical injury) [ 50 ]. Here, we also show that CSF and plasma Aβ42/Aβ40 levels are not influenced by the timing of the sample collection further supporting the use of Aβ42/Aβ40 ratio over Aβ42 alone in the diagnostic workup of AD. While the CSF and plasma levels of p-tau variants, NfL and GFAP did not exhibit diurnal variability, CSF levels of many synaptic and endo-lysosomal proteins were increased in samples collected in the evening. These results suggest an increase and build-up of markers associated with neuronal activity during wakefulness. In addition, our data highlight the need to consider the effects of circadian rhythms on the CSF (and potentially plasma) levels of synaptic and endo-lysosomal proteins that are considered as candidate biomarkers of AD. Overall, the findings of the present study support the standardization of sample collection protocols for AD biomarker determination, with sampling at a specific time interval during the day.

Data availability

Anonymized data from the study will be shared upon request from a qualified academic investigator.

Abbreviations

Alzheimer’s Disease

Amyloid precursor protein

Body mass index

Beta-synuclein

Cathepsin F

Cerebrospinal fluid

Chromogranin A

Food and Drug administration

False discovery rate

Estimated glomerular filtration rate

Glial fibrillary acidic protein

GM2 activator

In-vitro diagnostic

Liquid chromatography with tandem mass spectrometry

Lumbar puncture

Mean difference

Mass spectrometry

Mini-Mental State Examination

Neurofilament light

Neurogranin:

Neuronal pentraxin 1

Neuronal pentraxin receptor

Phosphatidylethanolamine-binding protein 1

Phosphorylated- tau

Secretogranin II

Syntaxin-1B

Neurosecretory protein VGF

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Open access funding provided by Lund University. Work at Lund University was supported by the Swedish Research Council (2022 − 00775), ERA PerMed (ERAPERMED2021-184), the Knut and Alice Wallenberg foundation (2017 − 0383), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer Foundation (AF-980907), the Swedish Brain Foundation (FO2021-0293), The Parkinson foundation of Sweden (1412/22), the Cure Alzheimer’s fund, the Konung Gustaf V: s och Drottning Victorias Frimurarestiftelse, the Skåne University Hospital Foundation (2020-O000028), Regionalt Forskningsstöd (2022 − 1259) and the Swedish federal government under the ALF agreement (2022-Projekt0080). Work at the University of Gothenburg was supported by grants from the Swedish Research Council (#2022 − 01018, #2019–02397, #2017 − 00915 and #2022 − 00732), the European Union’s Horizon Europe research and innovation programme under grant agreement No 101053962, and Swedish State Support for Clinical Research (#ALFGBG-71320 and #ALFGBG-965240). HZ is a Wallenberg Scholar. KB is supported by the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), and the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495).

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Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Sölvegatan 19, BMC B11, Lund, 221 84, Sweden

Anna Orduña Dolado, Erik Stomrud, Shorena Janelidze & Oskar Hansson

Memory Clinic, Skåne University Hospital, S:t Johannesgatan 8, Malmö, SE-20502, Sweden

Erik Stomrud & Oskar Hansson

Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

Nicholas J. Ashton, Johanna Nilsson, Wagner S. Brum, Ann Brinkmalm Westman, Henrik Zetterberg & Kaj Blennow

Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway

Nicholas J. Ashton

Department of Old Age Psychiatry, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, UK

NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK

Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

Johanna Nilsson, Ann Brinkmalm Westman, Henrik Zetterberg & Kaj Blennow

Roche Diagnostics International Ltd, Rotkreuz, Switzerland

Clara Quijano-Rubio

Roche Diagnostics GmbH, Penzberg, Germany

Alexander Jethwa

Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil

Wagner S. Brum

Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK

Henrik Zetterberg

UK Dementia Research Institute at UCL, London, UK

Hong Kong Center for Neurodegenerative Diseases, Hong Kong, Hong Kong SAR, China

Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA

Pitié-Salpêtrière Hospital, Paris Brain Institute, ICM, Sorbonne University, Paris, France

Kaj Blennow

Neurodegenerative Disorder Research Center, Division of Life Sciences and Medicine, Department of Neurology, Institute on Aging and Brain Disorders, University of Science and Technology of China and First Affiliated Hospital of USTC, Hefei, P.R. China

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Contributions

AOD analyzed and interpreted the data, drafted, and revised the manuscript. ES designed the study and helped acquire the data and revised the manuscript for content. NJA helped acquire the data and revised the manuscript for content. JN helped acquire the data and revised the manuscript for content. CQR helped acquire the data and revised the manuscript for content. AJ helped acquire the data and revised the manuscript for content. WSB helped acquire the data and revised the manuscript for content. ABW helped acquire the data and revised the manuscript for content. HZ helped acquire the data and revised the manuscript for content. KB helped acquire the data and revised the manuscript for content. SJ designed the study, helped acquire the data, supervised the analysis and interpretation of the data, and critically reviewed the manuscript. OH designed the study, helped acquire the data, supervised the analysis and interpretation of data and critically reviewed the manuscript.

Corresponding authors

Correspondence to Anna Orduña Dolado or Oskar Hansson .

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The study was approved by the Regional Ethics Committee in Lund, Sweden. Written informed consent was obtained from all participants.

Competing interests

CQ-R is a full‑time employee of Roche Diagnostics International Ltd. AJ is a full‑time employee of Roche Diagnostics GmbH. HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Acumen, Alector, Alzinova, ALZPath, Annexon, Apellis, Artery Therapeutics, AZTherapies, Cognito Therapeutics, CogRx, Denali, Eisai, Merry Life, Nervgen, Novo Nordisk, Optoceutics, Passage Bio, Pinteon Therapeutics, Prothena, Red Abbey Labs, reMYND, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant and at advisory boards for Acumen, ALZPath, BioArctic, Biogen, Eisai, Lilly, Moleac Pte. Ltd, Novartis, Ono Pharma, Prothena, Roche Diagnostics, and Siemens Healthineers; has served at data monitoring committees for Julius Clinical and Novartis; has given lectures, produced educational materials and participated in educational programs for AC Immune, Biogen, Celdara Medical, Eisai and Roche Diagnostics; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program, outside the work presented in this paper. OH has acquired research support (for the institution) from ADx, AVID Radiopharmaceuticals, Biogen, Eli Lilly, Eisai, Fujirebio, GE Healthcare, Pfizer, and Roche. In the past 2 years, he has received consultancy/speaker fees from AC Immune, Amylyx, Alzpath, BioArctic, Biogen, Cerveau, Eisai, Eli Lilly, Fujirebio, Merck, Novartis, Novo Nordisk, Roche, Sanofi and Siemens. The Roche NeuroToolKit is a panel of exploratory prototype assays designed to robustly evaluate biomarkers associated with key pathologic events characteristic of AD and other neurological disorders, used for research purposes only and not approved for clinical use. Elecsys β-amyloid(1–42) CSF is approved for clinical use. COBAS and ELECSYS are trademarks of Roche. All other product names and trademarks are the property of their respective owners. The rest of authors do not report any disclosures.

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Orduña Dolado, A., Stomrud, E., Ashton, N.J. et al. Effects of time of the day at sampling on CSF and plasma levels of Alzheimer’ disease biomarkers. Alz Res Therapy 16 , 132 (2024). https://doi.org/10.1186/s13195-024-01503-x

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Received : 08 April 2024

Accepted : 14 June 2024

Published : 22 June 2024

DOI : https://doi.org/10.1186/s13195-024-01503-x

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