It is oft observed in medicine that what works for one patient may not work for another. Determining for whom a treatment works and does not work is of great clinical interest. We propose a methodology to estimate treatment effect heterogeneity, i.e. to ascertain for which subpopulations a treatment is effective or harmful. The model studied assumes the relationship between an outcome of interest (e.g. blood pressure, cholesterol, survival) and a set of covariates (e.g. treatment, age, gender) is modified by a linear combination of a set of features (e.g. gene expression). Specifically a threshold on the linear combination divides the population into two subpopulations with different responses to treatment. Techniques from Latent Supervised Learning, a novel machine learning idea, are applied for model estimation, i.e. estimation of the linear combination and the corresponding threshold. Consistency of the estimator is established. In simulations the proposed methodology demonstrates high classification accuracy in a wide array of settings. Three data analysis examples are presented to illustrate the efficacy and applicability of the proposed methodology.
Wei, Susan and Kosorok, Michael R., "Latent Supervised Learning for Estimating Treatment Effect Heterogeneity" (July 2014). The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series. Working Paper 40.