Meta-analytic-predictive priors based on a single study

TL;DR

This paper discusses the use of meta-analytic-predictive priors derived from a single study, highlighting their implementation, interpretation, and importance in clinical applications within a Bayesian framework.

Contribution

It clarifies the application and interpretation of MAP priors when based on only one study, emphasizing careful prior specification and demonstrating practical implementation.

Findings

Single-study MAP priors can be effectively used with proper prior assumptions.

The normal-normal hierarchical model facilitates implementation of single-study MAP priors.

Application examples in clinical medicine illustrate the approach's practical utility.

Abstract

Meta-analytic-predictive (MAP) priors have been proposed as a generic approach to deriving informative prior distributions, where external empirical data are processed to learn about certain parameter distributions. The use of MAP priors is also closely related to shrinkage estimation (also sometimes referred to as dynamic borrowing). A potentially odd situation arises when the external data consist only of a single study. Conceptually this is not a problem, it only implies that certain prior assumptions gain in importance and need to be specified with particular care. We outline this important, not uncommon special case and demonstrate its implementation and interpretation based on the normal-normal hierarchical model. The approach is illustrated using example applications in clinical medicine.