Presentation: Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes
Speaker: Iván Díaz, Ph.D., Assistant Professor of Biostatistics, Weill Cornell Graduate School of Medical Sciences
Abstract: We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the conditional treatment effect, and recasting the problem in terms of weighted classification using the 0–1 loss function. Our estimated rules are ensembles that minimize the cross-validated risk of a linear combination in a user-supplied library of candidate estimators. We prove oracle inequalities bounding the finite-sample excess risk of the estimator. The bounds depend on the excess risk of the oracle selector and a doubly-robust term related to estimation of the nuisance parameters. We discuss the convergence rates of our estimator to the oracle selector, and illustrate our methods in the analysis of a phase III randomized study testing the efficacy of a new therapy for the treatment of breast cancer.