Bayesian Shrinkage Estimation of Credible Subgroups for Count Data with Excess Zeros

Heterogeneity of treatment effects due to heterogeneous patient characteristics often arises in clinical trials. Subgroup analysis and the analysis of interactions are the most common approaches for evaluating such heterogeneous effects but do not explicitly address multiplicity issues. Another common challenge of analyzing treatment effect heterogeneity is the large number of possible covariates which inevitably causes problems related to multiplicity and lack of power. In this article, we develop a Bayesian credible subgroups method using continuous shrinkage priors to assess heterogeneity in treatment effects and multiplicity-adjusted benefiting subgroup identification for zero-inflated count data, which are often encountered in medical and public health studies. Our proposed method provides two bounding subgroups for the true benefiting subgroup: one that is probably contained by the true benefiting subgroup and one that probably contains the true benefiting subgroup. A simulation study has been conducted to compare the performance of the proposed method with other methods through frequentist properties. We apply our method to a clinical bladder tumor trial studying the effect of thiotepa treatment on the reduction of the recurrence of bladder tumor.