Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.

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 the 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 crossvalidated risk of a linear combination in a user-supplied library of candidate estimators. We prove oracle inequalities bounding the finite-samp

SUBMITTER: Diaz I 

PROVIDER: S-EPMC6374011 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature