analysis and synthesis in research

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• Published: 08 March 2018

Meta-analysis and the science of research synthesis

• Jessica Gurevitch 1 ,
• Julia Koricheva 2 ,
• Shinichi Nakagawa 3 , 4 &
• Gavin Stewart 5  

Nature volume  555 ,  pages 175–182 ( 2018 ) Cite this article

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Meta-analysis is the quantitative, scientific synthesis of research results. Since the term and modern approaches to research synthesis were first introduced in the 1970s, meta-analysis has had a revolutionary effect in many scientific fields, helping to establish evidence-based practice and to resolve seemingly contradictory research outcomes. At the same time, its implementation has engendered criticism and controversy, in some cases general and others specific to particular disciplines. Here we take the opportunity provided by the recent fortieth anniversary of meta-analysis to reflect on the accomplishments, limitations, recent advances and directions for future developments in the field of research synthesis.

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Acknowledgements

We dedicate this Review to the memory of Ingram Olkin and William Shadish, founding members of the Society for Research Synthesis Methodology who made tremendous contributions to the development of meta-analysis and research synthesis and to the supervision of generations of students. We thank L. Lagisz for help in preparing the figures. We are grateful to the Center for Open Science and the Laura and John Arnold Foundation for hosting and funding a workshop, which was the origination of this article. S.N. is supported by Australian Research Council Future Fellowship (FT130100268). J.G. acknowledges funding from the US National Science Foundation (ABI 1262402).

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Department of Ecology and Evolution, Stony Brook University, Stony Brook, 11794-5245, New York, USA

Jessica Gurevitch

School of Biological Sciences, Royal Holloway University of London, Egham, TW20 0EX, Surrey, UK

Julia Koricheva

Evolution and Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, 2052, New South Wales, Australia

Shinichi Nakagawa

Diabetes and Metabolism Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney, 2010, New South Wales, Australia

School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK

Gavin Stewart

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Gurevitch, J., Koricheva, J., Nakagawa, S. et al. Meta-analysis and the science of research synthesis. Nature 555 , 175–182 (2018). https://doi.org/10.1038/nature25753

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• Synthesizing Sources | Examples & Synthesis Matrix

Synthesizing Sources | Examples & Synthesis Matrix

Published on July 4, 2022 by Eoghan Ryan . Revised on May 31, 2023.

Synthesizing sources involves combining the work of other scholars to provide new insights. It’s a way of integrating sources that helps situate your work in relation to existing research.

Synthesizing sources involves more than just summarizing . You must emphasize how each source contributes to current debates, highlighting points of (dis)agreement and putting the sources in conversation with each other.

You might synthesize sources in your literature review to give an overview of the field or throughout your research paper when you want to position your work in relation to existing research.

Table of contents

Example of synthesizing sources, how to synthesize sources, synthesis matrix, other interesting articles, frequently asked questions about synthesizing sources.

Let’s take a look at an example where sources are not properly synthesized, and then see what can be done to improve it.

This paragraph provides no context for the information and does not explain the relationships between the sources described. It also doesn’t analyze the sources or consider gaps in existing research.

Research on the barriers to second language acquisition has primarily focused on age-related difficulties. Building on Lenneberg’s (1967) theory of a critical period of language acquisition, Johnson and Newport (1988) tested Lenneberg’s idea in the context of second language acquisition. Their research seemed to confirm that young learners acquire a second language more easily than older learners. Recent research has considered other potential barriers to language acquisition. Schepens, van Hout, and van der Slik (2022) have revealed that the difficulties of learning a second language at an older age are compounded by dissimilarity between a learner’s first language and the language they aim to acquire. Further research needs to be carried out to determine whether the difficulty faced by adult monoglot speakers is also faced by adults who acquired a second language during the “critical period.”

Scribbr Citation Checker New

The AI-powered Citation Checker helps you avoid common mistakes such as:

• Missing commas and periods
• Incorrect usage of “et al.”
• Ampersands (&) in narrative citations
• Missing reference entries

To synthesize sources, group them around a specific theme or point of contention.

As you read sources, ask:

• What questions or ideas recur? Do the sources focus on the same points, or do they look at the issue from different angles?
• How does each source relate to others? Does it confirm or challenge the findings of past research?
• Where do the sources agree or disagree?

Once you have a clear idea of how each source positions itself, put them in conversation with each other. Analyze and interpret their points of agreement and disagreement. This displays the relationships among sources and creates a sense of coherence.

Consider both implicit and explicit (dis)agreements. Whether one source specifically refutes another or just happens to come to different conclusions without specifically engaging with it, you can mention it in your synthesis either way.

Synthesize your sources using:

• Topic sentences to introduce the relationship between the sources
• Signal phrases to attribute ideas to their authors
• Transition words and phrases to link together different ideas

To more easily determine the similarities and dissimilarities among your sources, you can create a visual representation of their main ideas with a synthesis matrix . This is a tool that you can use when researching and writing your paper, not a part of the final text.

In a synthesis matrix, each column represents one source, and each row represents a common theme or idea among the sources. In the relevant rows, fill in a short summary of how the source treats each theme or topic.

This helps you to clearly see the commonalities or points of divergence among your sources. You can then synthesize these sources in your work by explaining their relationship.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
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• Is ChatGPT trustworthy?
• Using ChatGPT for your studies
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• Common knowledge

Synthesizing sources means comparing and contrasting the work of other scholars to provide new insights.

It involves analyzing and interpreting the points of agreement and disagreement among sources.

You might synthesize sources in your literature review to give an overview of the field of research or throughout your paper when you want to contribute something new to existing research.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

Topic sentences help keep your writing focused and guide the reader through your argument.

In an essay or paper , each paragraph should focus on a single idea. By stating the main idea in the topic sentence, you clarify what the paragraph is about for both yourself and your reader.

At college level, you must properly cite your sources in all essays , research papers , and other academic texts (except exams and in-class exercises).

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.

The exact format of your citations depends on which citation style you are instructed to use. The most common styles are APA , MLA , and Chicago .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Ryan, E. (2023, May 31). Synthesizing Sources | Examples & Synthesis Matrix. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/working-with-sources/synthesizing-sources/

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A Guide to Evidence Synthesis: What is Evidence Synthesis?

• Meet Our Team
• Our Published Reviews and Protocols
• What is Evidence Synthesis?
• Types of Evidence Synthesis
• Evidence Synthesis Across Disciplines
• Finding and Appraising Existing Systematic Reviews
• 0. Develop a Protocol
• 1. Draft your Research Question
• 2. Select Databases
• 3. Select Grey Literature Sources
• 4. Write a Search Strategy
• 5. Register a Protocol
• 6. Translate Search Strategies
• 7. Citation Management
• 8. Article Screening
• 9. Risk of Bias Assessment
• 10. Data Extraction
• 11. Synthesize, Map, or Describe the Results
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• Open Access Evidence Synthesis Resources

What are Evidence Syntheses?

What are evidence syntheses.

According to the Royal Society, 'evidence synthesis' refers to the process of bringing together information from a range of sources and disciplines to inform debates and decisions on specific issues. They generally include a methodical and comprehensive literature synthesis focused on a well-formulated research question.  Their aim is to identify and synthesize all  of the scholarly research on a particular topic, including both published and unpublished studies. Evidence syntheses are conducted in an unbiased, reproducible way to provide evidence for practice and policy-making, as well as to identify gaps in the research. Evidence syntheses may also include a meta-analysis, a more quantitative process of synthesizing and visualizing data retrieved from various studies. 

Evidence syntheses are much more time-intensive than traditional literature reviews and require a multi-person research team. See this PredicTER tool to get a sense of a systematic review timeline (one type of evidence synthesis). Before embarking on an evidence synthesis, it's important to clearly identify your reasons for conducting one. For a list of types of evidence synthesis projects, see the next tab.

How Does a Traditional Literature Review Differ From an Evidence Synthesis?

How does a systematic review differ from a traditional literature review.

One commonly used form of evidence synthesis is a systematic review.  This table compares a traditional literature review with a systematic review.

Video: Reproducibility and transparent methods (Video 3:25)

Reporting Standards

There are some reporting standards for evidence syntheses. These can serve as guidelines for protocol and manuscript preparation and journals may require that these standards are followed for the review type that is being employed (e.g. systematic review, scoping review, etc). ​

• PRISMA checklist Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses.
• PRISMA-P Standards An updated version of the original PRISMA standards for protocol development.
• PRISMA - ScR Reporting guidelines for scoping reviews and evidence maps
• PRISMA-IPD Standards Extension of the original PRISMA standards for systematic reviews and meta-analyses of individual participant data.
• EQUATOR Network The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines. They provide a list of various standards for reporting in systematic reviews.

Video: Guidelines and reporting standards

PRISMA Flow Diagram

The  PRISMA  flow diagram depicts the flow of information through the different phases of an evidence synthesis. It maps the search (number of records identified), screening (number of records included and excluded), and selection (reasons for exclusion).  Many evidence syntheses include a PRISMA flow diagram in the published manuscript.

See below for resources to help you generate your own PRISMA flow diagram.

• PRISMA Flow Diagram Tool
• PRISMA Flow Diagram Word Template
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Learning about Synthesis Analysis

What D oes Synthesis and Analysis Mean?

Synthesis: the combination of ideas to

• show commonalities or patterns

Analysis: a detailed examination

• of elements, ideas, or the structure of something
• can be a basis for discussion or interpretation

Synthesis and Analysis: combine and examine ideas to

• show how commonalities, patterns, and elements fit together
• form a unified point for a theory, discussion, or interpretation
• develop an informed evaluation of the idea by presenting several different viewpoints and/or ideas

Key Resource: Synthesis Matrix

Synthesis Matrix

A synthesis matrix is an excellent tool to use to organize sources by theme and to be able to see the similarities and differences as well as any important patterns in the methodology and recommendations for future research. Using a synthesis matrix can assist you not only in synthesizing and analyzing,  but it can also aid you in finding a researchable problem and gaps in methodology and/or research.

Use the Synthesis Matrix Template attached below to organize your research by theme and look for patterns in your sources .Use the companion handout, "Types of Articles" to aid you in identifying the different article types for the sources you are using in your matrix. If you have any questions about how to use the synthesis matrix, sign up for the synthesis analysis group session to practice using them with Dr. Sara Northern!

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Analysis and Synthesis

• First Online: 18 February 2020

Cite this chapter

• Patricia A. Dwyer 3  

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Data analysis is a challenging stage of the integrative review process as it requires the reviewer to synthesize data from diverse methodological sources. Although established approaches to data analysis and synthesis of integrative review findings continue to evolve, adherence to systematic methods during this stage is essential to mitigating potential bias. The use of rigorous and transparent data analysis methods facilitates an evidence synthesis that can be confidently incorporated into practice. This chapter discusses strategies for data analysis including creating a data matrix and presents inductive analysis approaches to support the integration and interpretation of data from a body of literature. This chapter also discusses the presentation of results and includes examples of narrative and thematic syntheses from recently published integrative reviews.

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Dwyer, P.A. (2020). Analysis and Synthesis. In: Toronto, C., Remington, R. (eds) A Step-by-Step Guide to Conducting an Integrative Review. Springer, Cham. https://doi.org/10.1007/978-3-030-37504-1_5

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Why every student (and researcher) should know about evidence synthesis

The ability to collect, summarise and analyse data is especially vital for students and researchers in medical and healthcare-related fields, writes Alessio Bellato. Here, he explains what it is and how to access training

Alessio Bellato

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A diy guide to starting your own journal, universities, ai and the common good, artificial intelligence and academic integrity: striking a balance, create an onboarding programme for neurodivergent students.

In an era when fake news and AI-created photos and videos populate the digital world, the ability to recognise what is valid and solid evidence (and what is not) is increasingly important. This is particularly relevant in medical and healthcare-related disciplines (including psychology), because work in these fields directly affects the lives and well-being of many people. Students and researchers need to be able to bring together and assess data and results from multiple sources – synthesising evidence – to come to valid, evidenced conclusions and use them to inform their work.

In this article, I will discuss what evidence synthesis is, why it should be incorporated into undergraduate curricula and postgraduate training, and how free training can be sought and accessed.

What is evidence synthesis?

Evidence synthesis is defined as the process of systematically selecting, summarising and appraising data from multiple sources. It involves gathering data from past studies to draw conclusions, assess the support or refutation of theories, and identify research gaps that need addressing before solid conclusions can be drawn. The importance of evidence synthesis in fields such as psychology, psychiatry and healthcare has grown significantly.  Studies such as systematic reviews and meta-analyses are in fact instrumental in helping clinicians, policymakers and researchers make informed decisions and recommendations for clinical practice. For example, in the UK, experts within the National Institute for Health and Care Excellence (NICE) consult published evidence synthesis studies (or conduct their own) to develop clinical guidelines and evidence-based recommendations for health and care.

Why should I learn about evidence synthesis?

Knowledge about evidence synthesis has benefits for both students and educators. As a student (including undergraduates), mastery of evidence synthesis can significantly enhance your academic profile. If you have expertise in conducting these studies, highlight it on your CV. Attending training programmes on specific evidence synthesis methodologies can also expand your knowledge and network with potential co-authors and collaborators for future publications and projects.

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For lecturers, evidence synthesis can become a component of your teaching. Consider creating new modules or workshops for master’s and PhD students specifically focused on evidence synthesis. In my experience, these offerings have been in high demand, indicating a strong student interest in learning these skills at university. 

If you’re not an expert in evidence synthesis, you can still integrate it into your lectures using meta-analyses, umbrella reviews or narrative reviews as references to support concepts or theories. This approach will help students understand the importance of evidence synthesis and encourage them to consult these types of studies when working on their essays and research projects, ultimately improving the quality and value of their work.

How to conduct evidence synthesis – and what do I need?

Evidence synthesis can be done by using systematic or non-systematic approaches. When you conduct evidence synthesis systematically (for example, when you do a systematic review instead of a literature review), there are precise steps you follow (see Figure 1). 

First, formulate the research question (for example, what is the population, intervention or outcome of interest?) and prepare the protocol. Then, retrieve the papers from relevant databases based on your discipline and screen them one by one. Data extraction or coding and assessment of study quality and risk of bias will follow. All these steps require you to be familiar with the chosen area of research and the specific methodology you are using (I’ve included suggestions about free training opportunities in evidence synthesis further down in this article).

Synthesise the data to find the bigger picture and address questions such as: how many studies did I find? What do they look like in terms of design, sample/study characteristics and main findings? Are most studies in favour of or against my hypotheses? Report results with meta-analysis or thematic synthesis, if applicable. Finally, prepare infographics, lay summaries or videos, explaining your study and its importance in simple terms.

Evidence synthesis can be done anywhere in the world and is cost effective. I am based in the UK, but I have worked with researchers from Italy, Spain, France, Brazil, the USA, Canada, Malaysia, Singapore and South Korea. I recommend working with other students or academics with expertise in evidence synthesis; they will mentor and guide you through the stages of the project. You can use online software for screening (such as  Rayyan or  Covidence ) or data extraction ( Google Sheets ). Accessing the full text of some papers could be challenging, especially if you are not affiliated with any university or research centre, or if your institution is not well resourced. Networking with people working at different institutions, requesting inter-library loans or contacting the authors can help to overcome this hurdle.

How can I learn to do evidence synthesis?

Courses and online guides can help educators, students and researchers learn about evidence synthesis. Online and in-person evidence synthesis courses can be too advanced or costly for some students, especially those in resource-limited institutions or low- to middle-income countries. However, free training opportunities are available.

If you are new to evidence synthesis or unsure about the difference between a literature review and a systematic review, watch this three-minute video:  Evidence Synthesis – What is it and why do we need it?  

Some of you might be thinking about doing a systematic review or meta-analysis, but there are many other ways to synthesise evidence. The University of Melbourne Medicine, Dentistry and Health Sciences library provides  a comprehensive online guide to review types to help you better understand these methods (including reviews of reviews and qualitative methods for evidence synthesis).

If you are interested in learning more about systematic reviews and meta-analysis and the specific steps to completing such projects, watch  How to Conduct a Systematic Review and Write-Up in 7 Steps or join the online training course  Introduction to Systematic Review and Meta-Analysis . 

For those who have started working on a systematic review, the  Cochrane Handbook for Systematic Reviews of Interventions or the e-book  Doing Meta-Analysis with R: A Hands-On Guide dive deeply into the methodological aspects of these techniques. If you have funding available to support your training, type “training evidence synthesis 2024” into Google to search for courses or workshops.

Have I provided sufficient evidence to persuade you of the importance of evidence synthesis for students and researchers working in psychology, medicine or healthcare? I believe so, but what do you think?

Alessio Bellato is a lecturer in child and adolescent mental health at the University of Southampton and an honorary assistant professor of psychology at the University of Nottingham Malaysia.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter .

For further information from Alessio Bellato on this topic, listen to the Association for Child and Adolescent Mental Health podcast Evidence Synthesis Studies, and Autonomic Dysregulation and Self-injurious Thoughts and Behaviour or read the paper “Evidence-based child and adolescent mental health care: The role of high-quality and transparently reported evidence synthesis studies” .

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Synthesising the data

Synthesis is a stage in the systematic review process where extracted data, that is the findings of individual studies, are combined and evaluated.   

The general purpose of extracting and synthesising data is to show the outcomes and effects of various studies, and to identify issues with methodology and quality. This means that your synthesis might reveal several elements, including:  

• overall level of evidence  
• the degree of consistency in the findings  
• what the positive effects of a drug or treatment are ,  and what these effects  are  based on  
• how many studies found a relationship or association between two components, e.g. the impact of disability-assistance animals on the psychological health of workplaces

There are two commonly accepted methods of synthesis in systematic reviews:  

Qualitative data synthesis

• Quantitative data synthesis  (i.e. meta-analysis)  

The way the data is extracted from your studies, then synthesised and presented, depends on the type of data being handled.  

In a qualitative systematic review, data can be presented in a number of different ways. A typical procedure in the health sciences is  thematic analysis .

Thematic synthesis has three stages:

• the coding of text ‘line-by-line’
• the development of ‘descriptive themes’
• and the generation of ‘analytical themes’

If you have qualitative information, some of the more common tools used to summarise data include:  

• textual descriptions, i.e. written words  
• thematic or content analysis

Example qualitative systematic review

A good example of how to conduct a thematic analysis in a systematic review is the following journal article on cancer patients. In it, the authors go through the process of:

• identifying and coding information about the selected studies’ methodologies and findings on patient care
• organising these codes into subheadings and descriptive categories
• developing these categories into analytical themes

What Facilitates “Patient Empowerment” in Cancer Patients During Follow-Up: A Qualitative Systematic Review of the Literature

Quantitative data synthesis

In a quantitative systematic review, data is presented statistically. Typically, this is referred to as a  meta-analysis .

The usual method is to combine and evaluate data from multiple studies. This is normally done in order to draw conclusions about outcomes, effects, shortcomings of studies and/or applicability of findings.

Remember, the data you synthesise should relate to your research question and protocol (plan). In the case of quantitative analysis, the data extracted and synthesised will relate to whatever method was used to generate the research question (e.g. PICO method), and whatever quality appraisals were undertaken in the analysis stage.

If you have quantitative information, some of the more common tools used to summarise data include:  

• grouping of similar data, i.e. presenting the results in tables  
• charts, e.g. pie-charts  
• graphical displays, i.e. forest plots

Example of a quantitative systematic review

A quantitative systematic review is a combination of qualitative and quantitative, usually referred to as a meta-analysis.

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About meta-analyses

A systematic review may sometimes include a  meta-analysis , although it is not a requirement of a systematic review. Whereas, a meta-analysis also includes a systematic review.  

A meta-analysis is a statistical  analysis  that combines data from  previous  studies  to calculate an overall result.

One way of accurately representing all the data is in the form of a  forest plot . A forest plot is a way of combining the results of multiple studies in order to show point estimates arising from different studies of the same condition or treatment.

It is comprised of a graphical representation and often also a table. The graphical display shows the mean value for each study and often with a confidence interval (the horizontal bars). Each mean is plotted relative to the vertical line of no difference.

The following is an example of the graphical representation of a forest plot.

“File:The effect of zinc acetate lozenges on the duration of the common cold.svg”  by  Harri Hemilä  is licensed under  CC BY 3.0

Watch the following short video where a social health example is used to explain how to construct a forest plot graphic.

Forest Plots: Understanding a Meta-Analysis in 5 Minutes or Less (5:38 mins)

Forest Plots – Understanding a Meta-Analysis in 5 Minutes or Less  (5:38 min) by The NCCMT ( YouTube )

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Analysis and Synthesis

Why it matters: analysis and synthesis.

The skills of analysis and synthesis are vital to academic writing; they are necessary for you to establish your ethos with fellow scholars and professionals in your field. In academic writing, you must first research and understand the conversation around a specific topic before you can attempt to add to it. In order to understand this conversation, you need to locate credible and recent academic sources on the topic, analyze them individually, and then synthesize them in order to identify patterns, trends, gaps, limitations, etc.

In this unit, we will discuss the importance of analyzing each source individually, and then synthesizing them with other, similar sources to understand the current state of the research surrounding the topic. Basically, if you want to learn more about your field, you must read credible sources that are backed by primary and secondary research; you must learn to understand these sources in isolation as well as how they fit together.

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• http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
• http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
• 1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
• 2 Swat College of Nursing , Pakistan
• 3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
• 4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
• Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

• Aromataris E ,
• Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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NIHR Health Technology Assessment programme: Executive Summaries. Southampton (UK): NIHR Journals Library; 2003-.

NIHR Health Technology Assessment programme: Executive Summaries.

Evaluating meta-ethnography: systematic analysis and synthesis of qualitative research.

R Campbell , P Pound , M Morgan , G Daker-White , N Britten , R Pill , L Yardley , C Pope , and J Donovan .

Affiliations

Published: 2011 .

Methods for reviewing and synthesising findings from quantitative research studies in health care are well established. Although there is recognition of the need for qualitative research to be brought into the evidence base, there is no consensus about how this should be done and methods for synthesising qualitative research are at a relatively early stage of development.

Our aim was to undertake methodological research to evaluate meta-ethnography as a method for synthesising qualitative research studies in health and health care.

• Methods of synthesis for qualitative research

A review of methods of qualitative synthesis was undertaken to examine the ways in which meta-ethnography, first described by Noblit and Hare (Noblit G, Hare R. Meta-ethnography: synthesising qualitative studies. 11th edn. London: Sage Publications; 1988), was being used, and to identify what other methods of qualitative synthesis were available. A range of methods for synthesising qualitative research was identified; none has become established, but meta-ethnography was the most widely cited method. Methods of qualitative synthesis could be broadly categorised as integrative or interpretative. In integrative syntheses, data in the primary studies are considered comparable and can therefore be pooled or aggregated. Methods falling into this category include numeric methods that involve the systematic pooling of qualitative data as a precursor to a quantitative analysis, narrative methods and cross-case techniques. Interpretative syntheses entail an emic approach with concepts and explanatory frameworks emerging through a process of induction. Meta-ethnography and grounded theory are examples of methods included in this second category.

Meta-ethnography has been applied in ways consistent with the approach originally described by Noblit and Hare (1988), but it has also been used as a procedure within an all-embracing form of synthesis called meta-study. Critical interpretative synthesis has also been proposed as a new method which evolved from an attempt to use meta-ethnography to bring together findings from a large and methodologically diverse group of studies.

Identification and selection of studies for synthesis

For this research, two full-qualitative syntheses were conducted using meta-ethnography: the first a synthesis of qualitative studies of medicine-taking and the second a synthesis of studies exploring patients’ experiences of living with rheumatoid arthritis (RA). Searching the literature to identify studies for possible inclusion in a qualitative synthesis remains problematic. Our findings suggest that multiple search strategies should be employed and that hand-searching key journals is particularly worthwhile. The yield of relevant papers produced by electronic databases appeared to be topic dependent, suggesting that a variety should be searched. Similarly, the effectiveness of the electronic search strategy depended on the subject of the search, making it advisable to try more than one approach. Potentially relevant studies identified in literature searches, conducted in July and August 2002, were appraised using a modified version of the Critical Appraisal Skills Programme [Critical Appraisal Skills Program. (CASP) collaboration for qualitative methodologies. 1998 URL: www.casp-uk.net] questions for understanding qualitative research. Candidate papers for both of our syntheses were excluded only on the grounds of lack of relevance to the aims of the synthesis or because the work failed to employ qualitative methods of data collection and analysis. Papers were not excluded on quality grounds alone. Quantifiable data from the appraisal process showed inter-rater agreement to be low.

Reproducibility

Before proceeding with the full syntheses, the first four papers to be synthesised in each of the two topic areas were independently synthesised by two researchers to explore the reproducibility of the meta-ethnographic method. The findings from these two preliminary syntheses produced mixed evidence of reproducibility. There were broad similarities in interpretation, but differences of detail. It is not possible to predict whether the differences would have become more or less pronounced as more papers were drawn into the syntheses.

Medicine-taking synthesis

Thirty-eight studies were entered into the synthesis, one of which did not contribute to the final synthesis. Most of the papers were about medicine-taking for chronic illness. Studies were initially synthesised by reciprocal translation within groups of papers defined by the type of medication. This process produced diagrams and textual summaries of medicine-taking within each group. The textual summaries were then synthesised thematically across the medicine groups as a lines-of-argument synthesis and the models were synthesised to produce a general model of medicine-taking. The synthesis revealed a general caution about taking medicine, and that the practice of lay testing of medicines was widespread. People were found to take their medicine passively or actively or to reject it outright. Some, in particular clinical areas, were coerced into taking it. Those who actively accepted their medicine often modified the regimen prescribed by a doctor, without the doctor’s knowledge. This synthesis concluded that people do not take their medicines as prescribed because of concerns about the medicines themselves. ‘Resistance’ emerged from the synthesis as a concept that best encapsulates the lay response to prescribed medicines. It was suggested that a future policy focus should be on the problems associated with the medicines themselves and on evaluating the effectiveness of alternative treatments that some people use in preference to prescribed medicines.

Synthesis of studies of lay experiences of living with rheumatoid arthritis

This synthesis began with 29 papers. Four of these could not be synthesised, leaving 25 papers (describing 22 studies) contributing to the final synthesis. Most of the papers were concerned with the everyday experience of living with RA. Papers were synthesised chronologically. The earliest paper in the series was used as an index paper and each subsequent paper entered into the synthesis was compared with it and all preceding papers. The smaller number and coherence of papers in this synthesis permitted the reciprocal translation of findings for all papers to be tabulated. These reciprocal translations were then subject to a process of reordering and reanalysis. The final synthesis was presented as a textual distillation of the findings supported by novel tabular summaries of the needs of people with RA and the general and specific coping strategies that they deployed to accommodate the disease. This synthesis did not produce significant new insights, probably because the early papers in the area were substantial and theoretically rich, and later papers were mostly confirmatory.

• Conclusions

This methodological research, conducted between April 2002 and September 2004, has shown that meta-ethnography is an effective method for synthesising qualitative research. The meta-ethnographic method enables a body of qualitative research to be drawn together in a systematic way. The process of reciprocally translating the findings from each study into those from all the other studies in the synthesis, if applied rigorously, ensures that qualitative data can be combined. Following this essential process, the synthesis can then be expressed as a ‘line of argument’ that can be presented as text and in summary tables and diagrams or models. Meta-ethnography can produce significant new insights, as was achieved in the medicine-taking synthesis. However, as the synthesis of studies about lay experiences of arthritis showed, not all meta-ethnographic syntheses will lead to new insights. This does not signal failure, however, because, as our RA synthesis showed, the meta-ethnographic method is able to identify fields in which saturation has been reached and in which no theoretical development has taken place for some time, an outcome that can ensure that resources are not wasted in future. Both of our syntheses found that only a minority of the studies included referenced each other, suggesting that unnecessary replication occurred. In addition to being able to demonstrate what the cumulative knowledge is in a particular area, meta-ethnographic syntheses are also able to identify absences of knowledge and can reveal aspects of a body of literature that may have been obscured.

Meta-ethnography as described by Noblit and Hare (1988) is a highly interpretative method, and considerable immersion in the individual studies is necessary to achieve a synthesis. It is a method that places substantial demands upon the synthesiser and requires a high degree of qualitative research skill. Experience to date suggests that the inclusion of > ~40 papers would result in a trade-off between the breadth of included papers and the depth of scrutiny and thought invested in each stage of the synthesis. Meta-ethnography has great potential as a method of synthesis in qualitative health technology assessment, but it is still evolving and cannot, at present, be regarded as a standardised approach capable of application in a routinised way.

Funding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Research.

• Publication
• Fleeman N, Bagust A, Boland A, Dickson R, Dundar R, Moonan M, et al . Evaluating meta-ethnography: systematic analysis and synthesis of qualitative research. Health Technol Assess 2011; 15 (43). [ PubMed : 22176717 ]
• NIHR Health Technology Assessment programme

The Health Technology Assessment (HTA) programme, part of the National Institute for Health Research (NIHR), was set up in 1993. It produces high-quality research information on the effectiveness, costs and broader impact of health technologies for those who use, manage and provide care in the NHS. ‘Health technologies’ are broadly defined as all interventions used to promote health, prevent and treat disease, and improve rehabilitation and long-term care.

The research findings from the HTA programme directly influence decision-making bodies such as the National Institute for Health and Clinical Excellence (NICE) and the National Screening Committee (NSC). HTA findings also help to improve the quality of clinical practice in the NHS indirectly in that they form a key component of the ‘National Knowledge Service’.

The HTA programme is needs led in that it fills gaps in the evidence needed by the NHS. There are three routes to the start of projects.

First is the commissioned route. Suggestions for research are actively sought from people working in the NHS, from the public and consumer groups and from professional bodies such as royal colleges and NHS trusts. These suggestions are carefully prioritised by panels of independent experts (including NHS service users). The HTA programme then commissions the research by competitive tender.

Second, the HTA programme provides grants for clinical trials for researchers who identify research questions. These are assessed for importance to patients and the NHS, and scientific rigour.

Third, through its Technology Assessment Report (TAR) call-off contract, the HTA programme commissions bespoke reports, principally for NICE, but also for other policy-makers. TARs bring together evidence on the value of specific technologies.

Some HTA research projects, including TARs, may take only months, others need several years. They can cost from as little as £40,000 to over £1 million, and may involve synthesising existing evidence, undertaking a trial, or other research collecting new data to answer a research problem.

The final reports from HTA projects are peer reviewed by a number of independent expert referees before publication in the widely read journal series Health Technology Assessment .

Criteria for inclusion in the HTA journal series

Reports are published in the HTA journal series if (1) they have resulted from work for the HTA programme, and (2) they are of a sufficiently high scientific quality as assessed by the referees and editors.

Reviews in Health Technology Assessment are termed ‘systematic’ when the account of the search, appraisal and synthesis methods (to minimise biases and random errors) would, in theory, permit the replication of the review by others.

The research reported in this issue of the journal was commissioned by the National Coordinating Centre for Research Methodology (NCCRM), and was formally transferred to the HTA programme in April 2007 under the newly established NIHR Methodology Panel. The HTA programme project number is 06/90/12. The contractual start date was in May 2002. The draft report began editorial review in November 2010 and was accepted for publication in July 2011. The commissioning brief was devised by the NCCRM who specified the research question and study design. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HTA editors and publisher have tried to ensure the accuracy of the authors’ report and would like to thank the referees for their constructive comments on the draft document. However, they do not accept liability for damages or losses arising from material published in this report.

The views expressed in this publication are those of the authors and not necessarily those of the HTA programme or the Department of Health.

Editor-in-Chief: Professor Tom Walley CBE

Series Editors: Dr Martin Ashton-Key, Professor Aileen Clarke, Dr Tom Marshall, Professor John Powell, Dr Rob Riemsma and Professor Ken Stein

Associate Editor: Dr Peter Davidson

Included under terms of UK Non-commercial Government License .

• Cite this Page Campbell R, Pound P, Morgan M, et al. Evaluating meta-ethnography: systematic analysis and synthesis of qualitative research. 2011. In: NIHR Health Technology Assessment programme: Executive Summaries. Southampton (UK): NIHR Journals Library; 2003-.
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Deep learning based synthesis of MRI, CT and PET: Review and analysis

Affiliations.

• 1 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia. Electronic address: [email protected].
• 2 Monash Biomedical Imaging, Clayton VIC 3800, Australia.
• 3 Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia.
• 4 Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom.
• 5 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
• 6 Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia.
• PMID: 38052145
• DOI: 10.1016/j.media.2023.103046

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.

Keywords: Generative deep-learning models; Medical image synthesis; Pseudo-CT; Synthetic MR; Synthetic PET.

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Publication types

• Deep Learning*
• Image Processing, Computer-Assisted / methods
• Magnetic Resonance Imaging
• Positron-Emission Tomography
• Tomography, X-Ray Computed