title: Reader reaction to "a robust method for estimating optimal treatment regimes" by Zhang et al. (2012).
abstract: A recent article (Zhang et al., 2012, Biometrics 168, 1010-1018) compares regression based and inverse probability based methods of estimating an optimal treatment regime and shows for a small number of covariates that inverse probability weighted methods are more robust to model misspecification than regression methods. We demonstrate that using models that fit the data better reduces the concern about non-robustness for the regression methods. We extend the simulation study of Zhang et al. (2012, Biometrics 168, 1010-1018), also considering the situation of a larger number of covariates, and show that incorporating random forests into both regression and inverse probability weighted based methods improves their properties.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Jeremy M G
authors.initials: JMG
authors.lastname: Taylor
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Wenting
authors.initials: W
authors.lastname: Cheng
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Jared C
authors.initials: JC
authors.lastname: Foster
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology* / Q000628:therapy*; D002986:Clinical Trials as Topic / Q000379:methods*; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*
keywords: Optimal treatment regime; Random forests