Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 5 - Recent Development on Statistical Methods for Precision Medicine
Type: Invited
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #308163
Title: Targeted Learning of Causal Impact of Optimal Individualized Treatment Rules Based on Novel Sequentially Adaptive Designs
Author(s): Mark Van der Laan and Ivana Malenica*
Companies: University of California, Berkeley
Keywords: optimal dynamic treatment; TMLE; super-learning; online adaptive designs; time-series

We discuss a super-learning of an optimal individualized treatment rule based on observing a sample of n individuals over time, and a CV-TMLE of its mean outcome with inference. We then present sequential adaptive designs involving enrolling individuals over time, and setting the randomization probabilities in response to the observed data on previously enrolled subjects. In particular, we demonstrate the utility of surrogates to make such designs able to use data on subjects that have not reached their endpoint yet. We present online super-learner of optimal rule based on past data, a method for determining the randomization probabilities robustly, and a TMLE for the mean outcome under the learned optimal rule. Finally, we present online adaptive designs and corresponding estimators of the optimal rule and its mean outcome based on observing a unit specific single time series, or multiple time series across individuals, allowing for asymptotic inference in the number of time points. Methods are demonstrated with simulated data and real data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program