Shrinkage Estimation Methods for Subgroup Analyses

Subgroup analyses increasingly gain importance for pharmaceutical investigations. Conventional approaches for treatment effect estimation are controversial because of multiplicity and small sample sizes within the subsets. Hence, we consider shrinkage estimators, which combine the overall effect estimate with the estimate within a given subgroup by using a Bayesian framework. This article contains a short introduction to two shrinkage estimation approaches proposed by Dixon and Simon and by Simon. Our key contribution is to present methodical extensions to enlarge the applicabilities and provide solutions for some computational issues. Besides an application to a real dataset, we perform an extensive simulation study, in which the conventional and the shrinkage approaches are compared under different models and scenarios of a typical clinical phase III design. The simulation results clearly show that the shrinkage approaches provide much better estimates than the conventional approaches according to the mean square error and the interval range under nearly all considered investigation cases. Exceeding advantages can be observed in the case of small sample sizes and low interaction effects. Some issues occur to the width and coverage probability of the credibility intervals concerning particular variants of the shrinkage estimators.

Automated summary

This study discussed the increasing importance of subgroup analyses in pharmaceutical research, and compared the traditional methods with shrinkage methods for treatment effect estimation. The results showed that shrinkage methods outperformed traditional methods in applicability extension and solving computational problems.