Certainly. Here's a more academically rigorous and detailed version of the LibGuide page content for **"What and How to Do a Meta-Analysis"**, tailored for **graduate students, health sciences researchers, and academic faculty**. It uses more formal language, includes more methodological depth, and reflects scholarly expectations in evidence synthesis.
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## ✅ **LibGuide Template: What and How to Do a Meta-Analysis (Advanced Academic Level)**
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🧠 What is a Meta-Analysis?
A meta-analysis is a structured, quantitative method of synthesizing data from multiple independent primary studies that address a common research question. It is typically conducted as part of a systematic review, providing a statistical estimate of the overall effect size, while accounting for variations across studies. Meta-analyses are widely used in clinical medicine, public health, and health policy to inform evidence-based decision-making.
Meta-analytic techniques enhance the precision of effect estimates, assess consistency among findings (heterogeneity), and identify potential sources of between-study variability. The process adheres to a rigorous methodological framework to minimize bias and ensure reproducibility.
- Purpose: To statistically integrate results from individual studies to derive a pooled effect estimate with increased statistical power.
- Common Applications: Treatment efficacy, diagnostic test accuracy, risk factor associations, and prevalence estimates.
- Foundational Principle: Greater cumulative knowledge emerges from the systematic aggregation and analysis of multiple studies than from any single study alone.
🗂️ Methodological Framework: Conducting a Meta-Analysis
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1. Formulate a Precise Research Question
Use the PICO (Population, Intervention, Comparator, Outcome) framework to ensure clarity and focus. A well-defined question guides the eligibility criteria, search strategy, and analysis plan.
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2. Develop and Register a Protocol
A priori registration is strongly recommended to promote transparency and reduce risk of bias. Register protocols via PROSPERO or follow the PRISMA-P guidelines. Protocols should detail:
- Eligibility criteria (inclusion/exclusion)
- Search strategy and databases
- Risk of bias tools
- Statistical methods and planned subgroup analyses
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3. Conduct a Comprehensive Literature Search
Design a robust, reproducible search strategy in collaboration with a health sciences librarian. Use a combination of controlled vocabulary (e.g., MeSH, Emtree) and keywords across multiple databases (e.g., PubMed/MEDLINE, Embase, Cochrane Library, CINAHL, PsycINFO). Record:
- Search strings and filters applied
- Dates of search and updates
- Gray literature sources (e.g., clinical trial registries, dissertations)
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4. Study Selection and Screening
Conduct title/abstract screening followed by full-text review. Use software like Rayyan or Covidence for blinded, dual-reviewer screening. Resolve conflicts via consensus or third-party adjudication.
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5. Critical Appraisal and Risk of Bias Assessment
Apply validated tools appropriate for the study designs included:
- RoB 2 – Randomized controlled trials
- ROBINS-I – Non-randomized studies
- Newcastle-Ottawa Scale – Observational studies
- QUADAS-2 – Diagnostic accuracy studies
Document and report risk of bias assessments to inform sensitivity analysis and interpretation of results.
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6. Data Extraction
Extract relevant quantitative and qualitative data using standardized templates. Include:
- Study identifiers (authors, year, journal)
- Population characteristics
- Intervention and comparator details
- Outcome measures (continuous, dichotomous, time-to-event)
- Effect sizes, confidence intervals, and standard errors
Use dual, independent data extraction to reduce errors.
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7. Statistical Synthesis
Perform meta-analysis using software such as:
- RevMan – User-friendly for Cochrane-style reviews
- R (metafor, meta packages) – Highly flexible, publication-grade analysis
- Stata – Comprehensive statistical capabilities
Key steps:
- Choose an appropriate model (fixed-effect vs. random-effects)
- Assess heterogeneity using Cochran’s Q and I² statistics
- Investigate outliers and influence using sensitivity analyses
- Explore subgroup or meta-regression analyses, if applicable
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8. Assess Publication Bias
Use funnel plots, Egger’s test, or Begg’s test to detect potential publication bias. Consider small-study effects and selective outcome reporting.
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9. Interpret Results in Context
Discuss the strength and applicability of findings. Interpret confidence intervals, heterogeneity, and quality of evidence using tools like GRADE.
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10. Report According to PRISMA Guidelines
Ensure full reporting using the PRISMA 2020 checklist and flow diagram. Include:
- Comprehensive methods and search strategy
- Study selection and risk of bias tables
- Forest plots and other visual summaries
- Transparent discussion of limitations
🛠️ Tools & Software for Meta-Analysis
| Tool |
Function |
Link |
| RevMan |
Cochrane's review manager for systematic reviews |
RevMan |
| R (metafor/meta) |
Advanced meta-analysis scripting in R |
metafor |
| Stata |
Statistical computing with meta-analysis commands |
Stata Meta |
| Covidence |
Systematic review management (screening, extraction) |
Covidence |
| Rayyan |
Collaborative abstract and full-text screening |
Rayyan |
📚 Key Resources and Guidance Documents
