Simple subgroup approximations to optimal treatment regimes from randomized clinical trial data.
We consider the use of randomized clinical trial (RCT) data to identify simple treatment regimes based on some subset of the covariate space, A. The optimal subset, A, is selected by maximizing the expected outcome under a treat-if-in-A regime, and is restricted to be a simple, as it is desirable that treatment decisions be made with only a limited amount of patient information required. We consider a two-stage procedure. In stage 1, non-parametric regression is used to estimate treatment effects for each subject, and in stage 2 these treatment effect estimates are used to systematically evaluate many subgroups of a simple, prespecified form to identify A. The proposed methods were found to perform favorably compared with two existing methods in simulations, and were applied to prehypertension data from an RCT.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA and Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20852, USA jared.foster@nih.gov.
Jared C
JC
Foster
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Jeremy M G
JM
Taylor
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Niko
N
Kaciroti
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Bin
B
Nan
D003627:Data Interpretation, Statistical; D006801:Humans; D006973:Hypertension / Q000517:prevention & control; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D057285:Precision Medicine / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*
Optimal treatment regimes; Personalized medicine; Subgroup analysis; Variable selection