title: Generated effect modifiers (GEM's) in randomized clinical trials.
abstract: In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration.
authors.affiliation: Department of Child and Adolescent Psychiatry, New York University, 1 Park Ave., New York, NY 10016, USA and Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA eva.petkova@nyumc.org.
authors.forename: Eva
authors.initials: E
authors.lastname: Petkova
authors.affiliation: Department of Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA and Department of Child and Adolescent Psychiatry, New York University, 1 Park Ave., New York, NY 10016, USA.
authors.forename: Thaddeus
authors.initials: T
authors.lastname: Tarpey
authors.affiliation: Department of Child and Adolescent Psychiatry, New York University, 1 Park Ave., New York, NY 10016, USA.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Su
authors.affiliation: Department of Biostatistics, Mailman School of Public Health, Columbia University, 722 West 168th St., New York, NY 10032, USA.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Ogden
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; 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*; D012107:Research Design / Q000706:statistics & numerical data*
keywords: Biosignature; Moderator; Precision medicine; Treatment decision; Value