title: Regularized outcome weighted subgroup identification for differential treatment effects.
abstract: To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A.
authors.forename: Yaoyao
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A.
authors.forename: Menggang
authors.initials: M
authors.lastname: Yu
authors.affiliation: Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A.
authors.forename: Ying-Qi
authors.initials: YQ
authors.lastname: Zhao
authors.affiliation: Department of Operation Research and Financial Engineering, Princeton University, Princeton, New Jersey, U.S.A.
authors.forename: Quefeng
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A.|Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A.
authors.forename: Sijian
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A.
authors.forename: Jun
authors.initials: J
authors.lastname: Shao
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016896:Treatment Outcome
keywords: Comparative effectiveness; Heterogeneity of treatment effectiveness; Regularization; Subgroup; Variable selection