Dynamically borrowing strength from another study through shrinkage estimation

TL;DR

This paper introduces a Bayesian meta-analytic approach that allows for dynamically borrowing strength from related studies through shrinkage estimation, improving effect estimates especially in rare disease contexts with limited data.

Contribution

It proposes a novel Bayesian framework for shrinkage estimation in meta-analysis that accounts for heterogeneity and can be applied to combine diverse evidence sources.

Findings

Shrinkage estimates improve effect size accuracy with minimal additional data.

The method effectively supports small studies using external evidence.

Application demonstrated in Creutzfeld-Jakob disease and pediatric liver transplantation.

Abstract

Meta-analytic methods may be used to combine evidence from different sources of information. Quite commonly, the normal-normal hierarchical model (NNHM) including a random-effect to account for between-study heterogeneity is utilized for such analyses. The same modeling framework may also be used to not only derive a combined estimate, but also to borrow strength for a particular study from another by deriving a shrinkage estimate. For instance, a small-scale randomized controlled trial could be supported by a non-randomized study, e.g. a clinical registry. This would be particularly attractive in the context of rare diseases. We demonstrate that a meta-analysis still makes sense in this extreme two-study setup, as illustrated using a recent trial and a clinical registry in Creutzfeld-Jakob disease. Derivation of a shrinkage estimate within a Bayesian random-effects meta-analysis may substantially improve a given estimate even based on only a single additional estimate while accounting for potential effect heterogeneity between the studies. Alternatively, inference may equivalently be motivated via a model specification that does not require a common overall mean parameter but considers the treatment effect in one study, and the difference in effects between the studies. The proposed approach is quite generally applicable to combine different types of evidence originating e.g. from meta-analyses or individual studies. An application of this more general setup is provided in immunosuppression following liver transplantation in children.