🤖 AI Summary
Traditional random-effects meta-analysis suffers from substantial bias in heterogeneity estimation and overly wide confidence intervals when applied to small-scale evidence syntheses (2–4 studies). To address this challenge in small-sample medical meta-analysis, we propose a novel evidence synthesis framework leveraging within-trial subgroup data. Our approach jointly models subgroup-level effect estimates and introduces two improved DerSimonian–Laird–type heterogeneity estimators. We further enhance uncertainty quantification by incorporating Henmi–Copas variance correction and adaptive degrees-of-freedom t-distribution inference. Simulation studies demonstrate that the method markedly improves accuracy of heterogeneity estimation and achieves superior balance between confidence interval coverage probability and width across diverse subgroup effect structures and prevalence scenarios. Empirical application to real-world clinical examples confirms its robustness and practical utility.
📝 Abstract
Random-effects meta-analyses are widely used for evidence synthesis in medical research. However, conventional methods based on large-sample approximations often exhibit poor performance in case of very few studies (e.g., 2 to 4), which is very common in practice. Existing methods aiming to improve small-sample performance either still suffer from poor estimates of heterogeneity or result in very wide confidence intervals. Motivated by meta-analyses evaluating surrogate outcomes, where units nested within a trial are often exploited when the number of trials is small, we propose an inference approach based on a common-effect estimator synthesizing data from the subgroup-level instead of the study-level. Two DerSimonian-Laird type heterogeneity estimators are derived using the subgroup-level data, and are incorporated into the Henmi-Copas type variance to adequately reflect variance components. We considered t-quantile based intervals to account for small-sample properties and used flexible degrees of freedom to reduce interval lengths. A comprehensive simulation is conducted to study the performance of our methods depending on various magnitudes of subgroup effects as well as subgroup prevalences. Some general recommendations are provided on how to select the subgroups, and methods are illustrated using two example applications.