Mark van der Laan - UC Berkeley
- 4:00 pm on Thursday, February 20, 2014
- 5:00 pm on Thursday, February 20, 2014
- MCS 148
Title: Targeted Learning of Optimal Individualized Treatment Rules. Abstract: Suppose we observe n independent and identically distributed observations of a time-dependent random variable consisting of baseline covariates, initial treatment and censoring indicator, intermediate covariates, subsequent treatment and censoring indicator, and a final outcome. For example, this could be data generated by a sequentially randomized controlled trial, where subjects are sequentially randomized to a first line and second line treatment, possibly assigned in response to an intermediate biomarker, and are subject to right-censoring. We consider data adaptive estimation of an optimal dynamic multiple time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to only respond to a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. In addition, we provide a targeted minimum loss-based estimator of the mean outcome under the optimal rule, with corresponding statistical inference. Both estimation problems addressed contrasts from the current literature that relies on parametric assumptions. We also present a cross-validated TMLE estimators of data adaptive target parameters such as the mean outcome under a data adaptive fit of the optimal rule. Practical performance of the methods is demonstrated with some simulations.