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 the treatment indicator 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’s assessment when s/he presents for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. I will introduce optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate a strong effect modifier in an RCT setting. This is a parsimonious alternative to existing methods for developing individualized treatment decision rules, that combines baseline covariates into a single strong moderator of treatment effect called a Generated Effect Modifier (GEM). The GEM can be constructed and used in the framework of the classic linear model. While the meaning and the characteristics of “a moderator” of treatment effect are well understood when the outcome is linearly related to a predictor, this meaning is less obvious when the outcome is related to the predictors nonlinearly. A GEM for a flexible nonlinear model is presented as well. I will discuss similarities between the GEM approach and single index models (SIM). I will present an illustration using data from a RCT designed to discover biosignatures for treatment response to antidepressants.