Meta-analysis of few small studies in orphan diseases

TL;DR

This paper evaluates statistical methods for meta-analysis in rare diseases with few small studies, highlighting the effectiveness of Bayesian approaches with weakly informative priors for better interval estimation.

Contribution

It systematically compares frequentist and Bayesian procedures for meta-analysis in small-sample contexts, providing guidance on method selection in orphan disease research.

Findings

Bayesian credibility intervals with weakly informative priors have good coverage.

Knapp-Hartung method produces conservative but reliable intervals.

Normal quantile-based methods often underperform in small-sample scenarios.

Abstract

Meta-analyses in orphan diseases and small populations generally face particular problems including small numbers of studies, small study sizes, and heterogeneity of results. However, the heterogeneity is difficult to estimate if only very few studies are included. Motivated by a systematic review in immunosuppression following liver transplantation in children we investigate the properties of a range of commonly used frequentist and Bayesian procedures in extensive simulation studies. Furthermore, the consequences for interval estimation of the common treatment effect in random effects meta-analysis are assessed. The Bayesian credibility intervals using weakly informative priors for the between-trial heterogeneity exhibited coverage probabilities in excess of the nominal level for a range of scenarios considered. However, they tended to be shorter than those obtained by the Knapp-Hartung method, which were also conservative. In contrast, methods based on normal quantiles exhibited coverages well below the nominal levels in many scenarios. With very few studies, the performance of the Bayesian credibility intervals is of course sensitive to the specification of the prior for the between trial heterogeneity. In conclusion, the use of weakly informative priors as exemplified by half-normal priors (with scale 0.5 or 1.0) for log odds ratios is recommended for applications in rare diseases.