Meta-analysis is a statistical technique for combining the findings from independent studies. It provides a more precise estimate of the effects of a treatment or intervention by pooling data from multiple studies.
What is Meta-Analysis?
A meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about a body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis.
Why Use Meta-Analysis?
Individual studies often have limited statistical power to detect small but clinically important effects. Meta-analysis increases the statistical power by combining results from multiple studies, allowing researchers to:
- Resolve uncertainty when reports disagree
- Improve estimates of effect size
- Answer questions not posed by individual studies
- Generate new hypotheses
Steps in Conducting a Meta-Analysis
- Formulate the research question — Clearly define the population, intervention, comparison and outcome (PICO)
- Systematic literature search — Search multiple databases to identify all relevant studies
- Study selection — Apply inclusion and exclusion criteria consistently
- Data extraction — Extract relevant data from each study using a standardised form
- Assess study quality — Evaluate risk of bias in each included study
- Statistical analysis — Pool results using appropriate statistical models (fixed or random effects)
- Interpret and report results — Present forest plots, funnel plots, and sensitivity analyses
Fixed vs. Random Effects Models
The fixed effects model assumes that all studies are estimating the same underlying true effect. The random effects model allows for the true effect to vary across studies, accounting for between-study heterogeneity.
Assessing Heterogeneity
Heterogeneity refers to variability among studies. Common measures include the Q statistic and the I² statistic, which quantifies the percentage of variation across studies due to heterogeneity rather than chance.
RevMan Software
Review Manager (RevMan) is the software used for preparing and maintaining Cochrane Reviews. It provides tools for preparing protocol and review text, and for entering data and performing statistical analyses. Download RevMan here.