Meta-Analysis
A statistical technique combining results from multiple independent studies to produce a more precise estimate of treatment effect. Meta-analyses increase statistical power and can resolve conflicting individual findings. For peptide drugs, meta-analyses provide the most robust evidence of efficacy and safety.
Technical Context
Meta-analytical methods: fixed-effects models (assumes one true effect size underlying all studies — appropriate when heterogeneity is low), random-effects models (assumes a distribution of true effects — appropriate when studies have methodological or population differences, producing wider confidence intervals), and Bayesian meta-analysis (incorporates prior knowledge). Heterogeneity assessment: I² statistic (0% = no heterogeneity, 25% = low, 50% = moderate, 75% = high), Cochran's Q test, and prediction intervals (range of effects expected in future studies). Publication bias detection: funnel plot asymmetry, Egger's test, and trim-and-fill analysis. For GLP-1 RA meta-analyses, heterogeneity often arises from: different comparators, varying baseline HbA1c/BMI, different dose levels, and varying treatment durations. Subgroup analyses and meta-regression can explore sources of heterogeneity.