A general statistical framework for subgroup identification and comparative treatment scoring.

ABSTRACT: Many statistical methods have recently been developed for identifying subgroups of patients who may benefit from different available treatments. Compared with the traditional outcome-modeling approaches, these methods focus on modeling interactions between the treatments and covariates while by-pass or minimize modeling the main effects of covariates because the subgroup identification only depends on the sign of the interaction. However, these methods are scattered and often narrow in scope. In this article, we propose a general framework, by weighting and A-learning, for subgroup identification in both randomized clinical trials and observational studies. Our framework involves minimum modeling for the relationship between the outcome and covariates pertinent to the subgroup identificati

SUBMITTER: Chen S 

PROVIDER: S-EPMC5561419 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature