Covariate-adjusted value-guided subgroup identification via boosting

It is widely recognized that treatment effects could differ across subgroups of patients. Subgroup analysis, which assesses such heterogeneity, provides valuable information in developing personalized therapies. There has been extensive research developing novel statistical methods for subgroup identification. The recent contribution is a value-guided subgroup identification method that directly maximizes treatment benefit at the subgroup level for survival outcome, rather than relying on individual treatment effect estimation. In this paper, we first completed this framework by illustrating its application to continuous and binary outcomes. More importantly, we extended the original framework to account for the prognostic effects and named this new method Covariate-Adjusted Value-guided subgroup identification via boosting (CAVboost). The original method directly used the outcome to formulate the value function for subgroup identification. Since the outcome can further be decomposed as prognostic effects and treatment effects, specifying the prognostic effects as the covariates of a model for the outcome can single out the treatment effects and improve the power to detect them across subgroups. Our proposed CAVboost was based on this key idea. It used a covariate-adjusted treatment effect estimator, instead of the outcome itself, to formulate the value function for subgroup identification. CAVboost estimates the treatment effect by using covariates to account for the prognostic effects, which mimics the idea of using covariates in an ANCOVA estimator. We showed that CAVboost could effectively improve the subgroup identification capability for both continuous and binary outcomes.