Meta Analysis

Meta-analysis is a statistical technique used to combine data from multiple independent studies to generate a single estimate of effect and its associated uncertainty. In the context of health technology assessment, the source studies are typically randomized controlled trials (RCTs). Meta-analysis is applicable when multiple studies have evaluated the effect of an intervention (or risk factor) using the same outcome measure, and when these studies are sufficiently similar in terms of participants, interventions, settings, duration, and outcome definitions and measurements.

The process of meta-analysis involves:

Systematic Review: Before quantitative synthesis, a qualitative assessment is conducted to ensure the source studies are comparable. This includes evaluating the similarity of study designs and the homogeneity of their results.

– Heterogeneity Testing: Statistical tests are performed to assess the variability among the study results.

– Modeling: Results are analyzed using fixed effects and random effects models to account for potential differences between studies.

– Forest Plot: The combined results are often displayed graphically using a Forest plot, which visually summarizes the individual study results and the overall estimate.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Checklist provides guidelines for the reporting of meta-analyses to ensure clarity and transparency. The Cochrane Collaboration has developed systematic approaches for conducting meta-analyses of RCTs across various healthcare areas. Additionally, meta-analysis techniques have evolved to support indirect comparisons where direct head-to-head trial data is unavailable, enhancing the ability to draw meaningful conclusions from diverse sets of data.