Systematic review vs meta-analysis

The difference between a systematic review vs meta-analysis lies in purpose and method. A systematic review aims to identify, evaluate and synthesise all available studies on a specific question. It follows a rigorous, predefined protocol to minimise bias and ensure comprehensiveness.

A meta-analysis goes a step further by statistically combining results from multiple studies within a systematic review. This technique provides a quantitative summary and often produces a more robust estimate of effect size. Thus, while all meta-analyses include a systematic review, not all systematic reviews include a meta-analysis.

This blog post explores systematic review vs meta-analysis: key differences in function, application, pros and cons. It also provides a step-by-step guide on conducting each type of study. Moreover, it includes tips for preparing systematic reviews and meta-analyses for publication, such as editing support and recommended resources like books, platforms and software tools to enhance your research process. Finally, it contains a helpful glossary for terms relevant to systematic review and meta-analysis.

• What is a systematic review?
• What is a meta-analysis?
• How to conduct a systematic review?
• How to conduct a meta-analysis?
• Systematic review vs meta-analysis
• Systematic review vs meta-analysis examples
• Preparing systematic review and meta-analysis for publication
• Resources
• Glossary

What is a systematic review?

A systematic review is a research method that collects, evaluates and synthesises all available studies on a specific topic. Its purpose is to provide a comprehensive overview of the evidence, reducing bias through structured methods. Scholars use systematic reviews to summarise evidence in a particular field, often to inform policy, clinical guidelines or further research.

Researchers use systematic reviews when there is a need to collate existing studies to answer a focused question, especially in fields like medicine, psychology and social sciences. They are important because they summarise a large volume of research, offer clear conclusions and identify gaps in knowledge.

Pros of systematic reviews

• Provide a thorough and unbiased summary of research
• Improve decision-making in policy and practice by clarifying evidence
• Identify areas where further research is needed

Cons of systematic reviews

• Require extensive time and resources due to rigorous protocols
• May produce inconclusive findings if studies lack consistency
• Risk of publication bias, as positive results are more often published

What is a meta-analysis?

Researchers use meta-analyses when sufficient studies on a topic are available with compatible methodologies and measures. Meta-analyses are crucial in fields like healthcare, psychology and social sciences because they can clarify the strength of an intervention or relationship and guide evidence-based decision-making.

Pros of meta-analysis

• Increases statistical power, offering a more reliable effect size
• Resolves conflicting study results through aggregation
• Provides clear, quantitative summaries and supports robust conclusions

Cons of meta-analysis

• Limited by quality and consistency of included studies
• Subject to publication bias, as studies with significant findings are often published more frequently
• Can yield misleading results if studies differ widely in design

How to conduct a systematic review?

To conduct a systematic review, researchers follow a rigorous, 10-step process to minimise bias and ensure a comprehensive synthesis of evidence. Here are the essential steps:

1. Formulate a research question

Define a focused research question, for instance, by using the PICO framework (population, intervention, comparison, outcome) to ensure the review targets relevant evidence.

2. Develop a protocol

Outline a protocol detailing the review’s objectives, inclusion/exclusion criteria, search strategy and planned methods for data extraction and analysis. Register the protocol in a repository (e.g. PROSPERO) to promote transparency.

3. Conduct a comprehensive literature search

Search multiple databases (e.g. PubMed, Cochrane Library) and other sources (e.g. grey literature, reference lists) to identify all potentially relevant studies. Develop a systematic search strategy using keywords and Boolean operators.

4. Screen studies

Use the inclusion and exclusion criteria to screen studies in two stages. First, screen titles and abstracts to remove irrelevant studies. Then, assess full texts to confirm eligibility. Two reviewers usually perform this step independently to reduce bias.

5. Assess study quality

Evaluate the methodological quality and risk of bias in included studies using standard tools (e.g. Cochrane risk of bias tool, Newcastle-Ottawa scale). This step helps ensure the credibility of findings.

6. Extract data

Collect key information from each study, including study design, population characteristics, interventions, outcomes and results. Standardise data where possible to facilitate analysis.

7. Synthesise the data

Combine findings through either a narrative synthesis or, if data allows, a quantitative approach like a meta-analysis. In a narrative synthesis, summarise trends and themes, while in a meta-analysis, pool data statistically to calculate an overall effect.

8. Assess heterogeneity

Examine the variability among studies, particularly if considering a meta-analysis. Understanding heterogeneity (e.g. through I² statistics) helps determine how consistent the findings are across different studies.

9. Identify potential biases

Evaluate potential sources of bias, such as publication bias, using tools like funnel plots. Acknowledging biases is crucial for balanced interpretation.

10. Report and interpret findings

Present results transparently, following guidelines like PRISMA (preferred reporting items for systematic reviews and meta-analyses). Discuss the strength of evidence, limitations and implications for practice, policy or further research.

How to conduct a meta-analysis?

To conduct a meta-analysis, researchers follow a structured process to ensure rigorous and reliable results. Here are the key 10 steps:

1. Define the research question

Formulate a clear, specific question or hypothesis. Identify the population, intervention, comparison and outcome (PICO) elements to guide your focus.

2. Conduct a systematic literature search

Search relevant databases (e.g. PubMed, PsycINFO) for studies that fit your criteria. Use a variety of keywords and limit the results to studies with compatible methodologies and metrics.

3. Screen and select studies

Use inclusion and exclusion criteria to select studies for analysis. For example, include only studies with similar outcomes and measurement tools. Exclude studies that lack necessary data or have low methodological quality.

4. Extract data

Gather relevant data from each study, including sample size, effect size, confidence intervals and any potential moderators. Standardise effect sizes if studies use different metrics (e.g. converting outcomes to a common scale).

5. Assess study quality

Evaluate the methodological quality of included studies using criteria such as sample size, bias risk and design. Document any concerns, as they may affect the interpretation of your results.

6. Calculate effect sizes and conduct statistical analysis

Use statistical software to calculate a pooled effect size. Apply random– or fixed-effects models depending on study variability. The random-effects model is generally preferred if studies differ significantly in design or population.

7. Evaluate heterogeneity

Assess heterogeneity (variability among studies) using tests like Cochran’s Q and I² statistics. High heterogeneity indicates that factors beyond sampling error contribute to variation, which may influence your choice of model and interpretation.

8. Conduct sensitivity analyses

Perform additional analyses to test the robustness of your results. For instance, check how results change when excluding lower-quality studies or those with extreme outcomes.

9. Check for publication bias

Detect publication bias using methods like funnel plots. Publication bias can affect your findings if studies with significant results are more likely to be published.

10. Interpret and report results

Present your findings with appropriate caution, discussing the combined effect size, heterogeneity and potential limitations. Summarise key insights while acknowledging how limitations may influence the conclusions.

Systematic review vs meta-analysis

Systematic reviews and meta-analyses both aim to synthesise research but differ in function; systematic reviews provide an overview of all relevant studies on a topic, while meta-analyses quantitatively combine data from these studies to estimate an effect size. Both methods help clarify evidence, inform guidelines and identify gaps, though systematic reviews are more resource-intensive and can be inconclusive, whereas meta-analyses increase statistical power but rely on compatible studies.

Function

Systematic reviews aim to gather, evaluate and synthesise all available studies addressing a specific research question, offering a broad overview of the evidence without necessarily calculating a quantitative effect. They are useful for mapping out a field, identifying trends and highlighting knowledge gaps. Meta-analyses, by contrast, focus on combining quantitative data from multiple studies within a systematic review. This statistical approach produces a pooled effect size estimate, thereby quantifying the strength of evidence from existing research.

Application

Systematic reviews are commonly applied in disciplines like healthcare, psychology and social sciences, where researchers need a structured method to collate and interpret findings on a defined topic. They provide a comprehensive summary useful for creating clinical guidelines, informing policy or guiding future research. Meta-analyses are applied within systematic reviews when enough studies with compatible data and methodologies exist, particularly when researchers seek to achieve a more precise estimate of an intervention’s or exposure’s effect.

Pros

The primary advantages of systematic reviews include their ability to comprehensively synthesise existing research, help identify gaps in knowledge and provide a broad overview of findings, which is beneficial for summarising large volumes of research. Meta-analyses, on the other hand, increase statistical power by pooling data across studies, offering a more precise and robust estimate of effect size. They can resolve inconsistencies across studies and quantify results, providing clearer conclusions.

Cons

Systematic reviews can be resource-intensive, requiring significant time and effort due to comprehensive searching, screening and critical appraisal. They may yield inconclusive results if included studies are too heterogeneous and can be prone to publication bias if unpublished studies are overlooked. Meta-analyses also have limitations, requiring that studies use similar measures, which can limit applicability. They are also vulnerable to publication bias, as studies with positive results are more likely to be published and high heterogeneity across studies may lead to misleading conclusions.

Tools

Common tools for conducting systematic reviews include PRISMA guidelines for structured reporting, Covidence for screening and management, EndNote for reference management, Rayyan for study selection and PROSPERO for protocol registration. Meta-analyses rely on tools like RevMan and Comprehensive Meta-Analysis (CMA) software, as well as statistical packages like R and STATA for advanced analysis, including forest plots and heterogeneity testing. Together, these tools support the rigorous demands of both systematic reviews and meta-analyses.

Aspect	Systematic review	Meta-analysis
Function	Collect, evaluate and synthesise all available studies on a specific question	Statistically combine data from multiple studies to estimate effect size
Application	Used to summarise and interpret a body of evidence on a specific question	Used within systematic reviews to produce a quantitative summary
Pros	Comprehensive synthesis Identifies knowledge gaps Informs guidelines	Increases precision Resolves inconsistencies Quantifies effect size
Cons	Time- and resource-intensive Can be inconclusive with heterogeneity Potential publication bias	Needs compatible studies Prone to publication bias High heterogeneity affects reliability
Tools	PRISMA, Covidence, EndNote, Rayyan, PROSPERO	RevMan, CMA, JBI SUMARI, R, STATA

Systematic review vs meta-analysis examples

Here are some examples of how researchers might approach a topic as a systematic review vs meta-analysis:

Efficacy of cognitive-behavioural therapy (CBT) for anxiety disorders

• Systematic review: A researcher conducts a systematic review to summarise all available studies on CBT for anxiety, examining various study designs, populations and outcomes to assess whether CBT generally helps reduce anxiety symptoms.
• Meta-analysis: In a meta-analysis, the researcher combines statistical data from studies on CBT’s impact on anxiety symptoms. This approach provides an aggregate effect size, indicating CBT’s average effectiveness across studies.

Impact of physical activity on heart disease risk

• Systematic review: A systematic review summarises all research on physical activity’s relationship with heart disease risk, including observational studies, randomised trials and reviews. This overview assesses trends and common findings.
• Meta-analysis: A meta-analysis calculates a pooled effect size for heart disease risk reduction based on physical activity levels, giving a precise estimate of how much physical activity lowers risk across studies with similar measures.

Effectiveness of online learning compared to traditional classroom learning

• Systematic review: A systematic review collates all studies comparing online and traditional learning, examining factors such as student outcomes, engagement levels and course completion rates to understand broader trends and patterns.
• Meta-analysis: In a meta-analysis, the researcher combines outcome data (e.g. exam scores) from comparable studies, providing a quantitative estimate of the performance difference between online and traditional learning environments.

Association between air pollution and asthma incidence in urban areas

• Systematic review: This review identifies all studies exploring the link between urban air pollution and asthma, evaluating varied methodologies and outcomes to determine if a consistent association exists.
• Meta-analysis: A meta-analysis combines specific data, such as relative risk ratios from studies, to determine an overall risk increase in asthma incidence per pollution level, yielding a precise, quantitative relationship.

Preparing systematic review and meta-analysis for publication

Professional editing services — developmental editing, line editing and copyediting — can significantly enhance the quality and readability of systematic reviews and meta-analyses, aiding in their successful publication. Here is how these services, centred around key goals like clarity, coherence, consistency and accuracy, contribute to preparing an academic manuscript for publication:

Clarity

Clarity is essential in systematic reviews and meta-analyses to ensure that readers can easily understand complex methodologies and results. Line editors can refine language and structure, eliminating ambiguous or overly technical phrasing. This helps the author present a clear research question, methodological process and findings, which is critical for readers interpreting large-scale evidence.

Coherence

Coherence is necessary to guide readers logically through a systematic review or meta-analysis. Developmental editors can enhance coherence by ensuring logical flow between sections, aligning findings with the review’s objectives and connecting ideas across paragraphs. This makes it easier for readers to follow the rationale behind study selection, methodology and conclusions, ultimately strengthening the study’s impact and accessibility.

Consistency

Consistency in terminology, style and formatting is essential in academic publications, as it fosters a professional, unified presentation. Copyeditors can ensure that terms, abbreviations and reference styles remain uniform throughout the manuscript, helping readers navigate complex information without distraction. This includes maintaining consistent use of statistical terms and units of measurement, which is vital in research involving data synthesis.

Accuracy

Accuracy is crucial to avoid misrepresenting data, misinterpreting findings or introducing errors in citations. Copyeditors can verify data presentation, such as tables and figures, to confirm that results align with the narrative and are accurately represented. They also check for correct citation of sources, reducing the risk of inaccuracies that could undermine the study’s reliability and credibility.

Conciseness

Conciseness ensures that every part of the manuscript contributes directly to the study’s objectives. Line editors can streamline the manuscript, removing redundant explanations or excessive details. This helps maintain focus on essential content, increasing the manuscript’s readability and appeal to journal reviewers and readers.

Adherence to style guidelines

Following the target journal’s style guidelines is essential for publication. Copyeditors ensure compliance with these guidelines, covering formatting requirements, citation styles and structure. This adherence can save time during the review process and reduce the likelihood of manuscript rejection on technical grounds.

Resources

Here are some valuable resources for writing systematic reviews and meta-analyses:

Books

• Systematic Reviews in Health Care: A Practical Guide by Matthias Egger, George Davey Smith and Douglas Altman introduces conducting systematic reviews in healthcare.
• Introduction to Meta-Analysis by Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins and Hannah R. Rothstein helps understand and conduct meta-analyses across disciplines.
• The Handbook of Research Synthesis and Meta-Analysis by Harris Cooper, Larry V. Hedges and Jeffrey C. Valentine explains methods for research synthesis and advanced meta-analysis techniques.
• Doing a Systematic Review: A Student’s Guide by Angela Boland, M. Gemma Cherry and Rumona Dickson provides a beginner-friendly approach to systematic reviews.

Platforms

• Cochrane Library is a leading database and resource centre for systematic reviews, particularly in health and medicine.
• PROSPERO is a registry for systematic review protocols that promotes transparency and helps prevent duplication in reviews.
• PRISMA (Preferred reporting items for systematic reviews and meta-analyses) offers guidelines, checklists and flow diagrams to ensure comprehensive reporting in systematic reviews.
• JBI SUMARI is the Joanna Briggs Institute’s systematic review software platform for evidence synthesis in health, social and behavioural sciences.

Podcasts

• The Evidence-Based Medicine Podcast yb Oxford University discusses various aspects of evidence-based medicine, including systematic reviews and meta-analyses, with insights from leading practitioners.
• Cochrane Library podcasts offers discussions with experts on methods, challenges and insights into conducting systematic reviews in health and medicine.
• The Research Rundown explains systematic reviews, meta-analyses and other research methods, with accessible insights for beginners and seasoned researchers alike.

Glossary for systematic reviews and meta-analyses

• Boolean operators: Words (e.g. AND, OR, NOT) used in search strategies to combine keywords and refine literature searches, essential in systematic review searches.
• Effect size: A quantitative measure of the strength of a phenomenon. In meta-analysis, effect size is often calculated to determine the overall impact of an intervention or relationship.
• Fixed-effects model: A statistical approach in meta-analysis that assumes all studies measure the same underlying effect. Used when study variability is low.
• Heterogeneity: The degree of variability or differences among studies included in a meta-analysis. High heterogeneity suggests substantial differences in study methods or populations, affecting the reliability of combined results.
• Publication bias: A tendency for studies with positive or significant results to be published more frequently than those with null or negative results, potentially skewing meta-analysis results.
• Random-effects model: A statistical approach in meta-analysis that assumes variability between studies and calculates an average effect size. Preferred when heterogeneity is high.
• Sensitivity analysis: An analysis in meta-analyses that tests the stability of results by excluding certain studies or using different statistical methods to ensure findings are robust.

Key takeaways

In conclusion, understanding the differences in purpose, methods and applications of systematic review vs meta-analysis is essential for producing high-quality research that informs evidence-based practice. By following structured steps and utilising specialised tools, researchers can effectively synthesise existing studies, provide valuable insights and advance knowledge in their fields.

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