Yes. Plan a question, gather studies, extract data, assess bias, pool effect sizes, probe heterogeneity, test stability, and report with PRISMA.
Meta-analysis turns a stack of studies into one clear estimate, so your literature review moves from a summary to a numeric answer with transparent steps that anyone can check.
Doing A Meta-Analysis For A Literature Review: The Plan
You’re writing a literature review and you’ve got a pile of eligible studies. A meta-analysis lets you combine their results, increase precision, and present a pooled effect with confidence intervals. The plan below keeps the process fast, traceable, and friendly for peer review.
Workflow Overview
| Stage | What You’ll Do | Output |
|---|---|---|
| Protocol | Define PICOS, outcomes, comparators, time windows, and analysis plan; pre-register if needed. | A dated protocol or PROSPERO record |
| Search | Build database strings, run grey literature checks, set date limits, and record all sources. | Search strings and logs |
| Screen | Apply inclusion and exclusion rules with two reviewers; resolve conflicts. | Study list with reasons for exclusion |
| Extract | Capture design, arms, counts, means, SDs, effect-ready numbers, and risk-of-bias items. | Clean extraction sheet |
| Synthesize | Choose effect measures, compute variances, pick a model, and weight studies. | Pooled estimate with CI |
| Diagnose | Quantify heterogeneity (I², τ²), inspect forest and funnel plots, run sensitivity checks. | Diagnostics and rationale |
| Report | Write methods and results to match a reporting checklist, share data and code. | PRISMA-ready write-up |
For reporting standards, use the PRISMA 2020 checklist. For methods, rely on the Cochrane Handbook chapter on meta-analysis. If your field expects registration, create a record with PROSPERO.
Core Concepts You’ll Use
Effect size. Pick a measure that matches your data. Binary outcomes often use risk ratio or odds ratio; time-to-event data use hazard ratio; continuous outcomes use mean difference or standardized mean difference when scales differ. Report the scale so readers can interpret direction and magnitude.
Variance and weights. Each study carries a weight based on its variance. Lower variance means a larger weight. With a fixed-effect model, weights reflect within-study variance. With a random-effects model, weights also include between-study variance (τ²), which spreads weight more evenly.
Heterogeneity. Studies rarely estimate the same effect. Cochran’s Q tests dispersion; I² describes the share of total variation not due to sampling error; τ² is the variance of true effects. I² near 0% points to little between-study variation; 25%, 50%, and 75% are often used as rough thresholds. Always pair these numbers with subject-matter reasoning.
Small-study effects. Asymmetry in a funnel plot can hint at publication bias or genuine size effects. Use Egger’s test with care, since it reacts to heterogeneity and a small number of studies.
Model choice. Random-effects models fit most literature reviews where design, setting, or measures differ. Fixed-effect models fit tight sets of trials with near-identical methods and a narrow question. State your choice and the reason, then stick to it unless a pre-planned sensitivity run says otherwise.
Steps For Meta-Analysis In A Literature Review
Set The Question And Outcomes
Frame the review with PICOS: population, intervention or exposure, comparator, outcomes, and study design. Define primary and secondary outcomes, time points, and any subgroups before you start. Write short, testable statements that map straight to data you can extract.
Register The Plan
Many journals and grad committees look for a dated protocol. Registering a record with PROSPERO takes little time and deters outcome switching. Keep a copy in your repository so readers can see any later deviations.
Build A Reproducible Search
Search across at least two major databases plus a preprint or trials register if relevant. Create a tested string with subject headings and keywords, capture the date, and export results with citation keys. Store the raw files so anyone can rerun the search. Note language limits only when they make sense for your topic.
Screen Titles, Abstracts, And Full Texts
Use two independent reviewers for each stage. Resolve conflicts with a third person or a rule set you wrote in the protocol. Track reasons for exclusion at full-text stage. Your PRISMA flow diagram will pull from these counts.
Extract Clean, Comparable Data
Design a form that mirrors your outcomes. Capture group sizes, event counts, means, SDs, follow-up length, analysis type, and any imputation. Pull adjusted estimates when the outcome is time-to-event or when confounding is clear. Record units and convert them early so you don’t mix scales later.
Assess Risk Of Bias
Pick a tool that fits the design mix: RoB 2 for randomized trials, ROBINS-I for non-randomized studies. Rate domains, write short justifications, and avoid a single overall label if it hides major concerns. Plan a sensitivity run that drops studies at high risk in critical domains.
Compute Effect Sizes
Turn the extracted numbers into comparable effects. For binary outcomes, calculate log risk ratios or log odds ratios with standard errors. For continuous outcomes, use mean difference or standardized mean difference with a small-sample correction when needed. For time-to-event data, extract log hazard ratios and standard errors from reported statistics, digitized curves, or author replies.
Choose And Fit The Model
Set the model from the protocol. With random-effects, pick a τ² estimator that matches your software and study count (REML works well in many cases). Run the primary model, then fit pre-planned subgroups or meta-regression if you have enough studies per covariate. Avoid post-hoc fishing; keep each extra run tied to a stated rationale.
Run Checks And Visuals
Produce a forest plot with study weights and CIs. Add influence diagnostics, leave-one-out runs, and Baujat plots if your software allows them. Create a funnel plot and apply a test for asymmetry only when you have a reasonable number of studies.
Rate Certainty And Write It Up
Summarize effect sizes and the range you see across studies. State the practical meaning in the units readers expect. Explain how risk of bias, imprecision, inconsistency, and reporting issues shape the take-home message. Map your sections to PRISMA items so a reader can tick off each one without guessing.
Doing A Meta-Analysis For A Literature Review: Hands-On Details
Data Prep That Saves Hours
Name variables consistently across studies. Keep one row per arm when computing effects, then pivot to one row per study once you’ve got the effect and its standard error. Add flags for subgroups and risk-of-bias domains so you can filter in seconds.
Picking Effect Measures That Fit
Use risk ratio when event rates are common and you want intuitive interpretation. Pick odds ratio when case-control designs or logistic models dominate. Choose standardized mean difference when scales differ and you still need a single pooled estimate. State your choice next to the forest plot so readers don’t need to hunt.
Interpreting Heterogeneity Without Myths
I² reflects dispersion, not study quality. A broad CI on I² is common with a small set of studies, so avoid rigid cutoffs. Report τ² with its estimator, give the prediction interval, and explain what range a new study might show.
Common Pitfalls And Quick Fixes
| Problem | What You’ll Notice | Quick Fix |
|---|---|---|
| Unit mismatch | Means in mg mixed with μg; follow-up windows don’t align. | Convert units; standardize time points or pool change scores. |
| Double-counting | Two arms from one study inflate weight. | Split the shared control or combine arms before pooling. |
| Zero cells | RR or OR undefined in rare events. | Add a continuity correction or use exact or Peto methods where suitable. |
| Imputed SDs only | Missing variance stalls pooling. | Derive SDs from CIs, SEs, P values, or contact authors. |
| Outcome switching | Reported outcomes don’t match the protocol. | Flag deviations; run a sensitivity set on pre-specified outcomes. |
| Outlier sway | One large study drives the result. | Check influence; report leave-one-out results next to the main model. |
| Small-study bias | Funnel asymmetry with large effects in tiny trials. | Report the pattern; avoid over-interpretation; use trim-and-fill with caution. |
Transparent Reporting That Readers Trust
Methods Section That Matches The Work
State databases, last search date, all limits, and the full strings in an appendix. Describe screening software, reviewer counts, and how you handled disagreements. Name the risk-of-bias tool and how you used its judgments in synthesis.
Results Section That Answers The Question
Start with the PRISMA flow, then the study table. Present the main forest plot early. Include core numbers in text: the pooled effect, CI, prediction interval, study count, and I² with τ². Point readers to subgroup or sensitivity results only if those runs were pre-planned.
Data, Code, And Reproducibility
Share a clean CSV and the script that runs the model and builds the figures. Mask any fields that carry personal data from individual participants in case reports. State software versions so others can reproduce the exact numbers.
Final Checks Before You Submit
A Short Walkthrough
Read the abstract as if you were the target reader. Scan headings to see if each piece lands in a logical order. Check that tables and figures stand on their own with clear captions. Confirm that your conclusion sentence in the abstract mirrors the pooled effect and its precision.
Ethics, Conflicts, And Funding
Declare funding sources and any links to study authors in the set you pooled. If you contacted authors for extra data, state this briefly. If your review could sway policy or purchasing, add a line on how you guarded against bias.
Where To Put Extra Material
Move long search strings, the full extraction form, and extra plots to a supplement. Keep the main text tight and readable while still mapping to each PRISMA item.
Quick Math: From Study Table To Effect Size
Binary Outcome Walkthrough
Say a trial reports 24/100 events with the intervention and 36/100 with control. Compute the risk in each arm (0.24 and 0.36), then the risk ratio (0.24 / 0.36 = 0.67). Take the natural log to pool on an additive scale (ln RR = −0.40). The standard error comes from the usual cell-count formula. You’ll pool log values, then back-transform the pooled log RR to the RR by exponentiation.
Continuous Outcome Walkthrough
When studies use the same units, pool mean difference. If scales differ, compute standardized mean difference. Hedges g uses a small-sample correction that reduces bias. Extract group means, SDs, and sizes, compute the pooled SD, then the difference in means divided by that pooled SD. Record the direction so larger scores always indicate improvement or harm in the same way across studies.
Time-To-Event Outcome Walkthrough
When a paper reports a hazard ratio with a CI, convert that CI to a standard error on the log scale and you’re ready to pool. When only a Kaplan–Meier curve appears, software can digitize points and recover an approximate log hazard ratio. Document the steps and mark such estimates so readers can gauge influence in sensitivity runs.
Subgroups And Meta-Regression That Respect The Data
Subgroups can clarify patterns across settings, doses, or study quality. Keep them prespecified and limit the count. Split by a small number of clear categories that map to real decisions. Meta-regression uses study-level covariates, so it can’t replace patient-level models. Use it as a descriptive tool with wide intervals, and avoid causal claims.
Software That Makes The Work Repeatable
R with packages such as metafor, meta, and dmetar gives you fine control, clear output, and scripts that anyone can run. RevMan offers a path for standard designs. Stata and Python libraries run common models. Whatever you pick, commit a script that reads the extraction sheet, computes effect sizes, fits the model, and exports figures. Version your files so you can trace every number back to the raw extraction.
Presentation That Wins Readers
Clear Figures
Place the main forest plot near the top of the results. Use labels that include outcome, metric, and time point. If you have many outcomes, give each one its own figure instead of cramming everything into a single panel.
Tables That Pull Their Weight
Provide one table for study characteristics and one for risk of bias. In the study table, include year, setting, sample size, follow-up, outcome definition, and which analysis set study reported. Link figure numbers to table rows so readers can hop between them.