Meta-analysis with a single study.

Area of Science:

• Biostatistics
• Medical Research Methodology

Background:

• Effect sizes vary across studies of the same intervention, a source of variation accounted for in random effects meta-analysis.
• Single studies cannot account for this heterogeneity, leading to underestimation of uncertainty when study-level effects are treated as population-level effects.

Purpose of the Study:

• To propose an empirical Bayesian approach to improve uncertainty estimation in single-trial analyses.
• To leverage data from existing meta-analyses to provide prior information for single-study evaluations.

Main Methods:

• Estimated population-level effect and heterogeneity distributions from 1635 meta-analyses in the Cochrane Database of Systematic Reviews.
• Assessed the performance of these distributions as priors in single-trial analyses using synthetic data and cross-validation.

Main Results:

• The proposed Bayesian approach significantly outperforms naive methods that assume non-varying effects.
• Priors on heterogeneity improved uncertainty quantification.
• Priors on treatment effects reduced mean squared error for both study-level and population-level effect estimation, with population-level gains equivalent to doubling sample size.

Conclusions:

• Empirical Bayesian meta-analyses of single studies offer a superior method for analyzing individual trials.
• This approach enhances the accuracy of uncertainty estimation and effect size evaluation, particularly beneficial when dealing with limited data.