Estimating the Optimal Personalized Treatment Strategy Based on Selected Variables to Prolong Survival via Random...

A personalized treatment policy requires defining the optimal treatment for each patient based on their clinical and other characteristics. Here we consider a commonly encountered situation in practice, when analyzing data from observational cohorts, that there are auxiliary variables which affect both the treatment and the outcome, yet these variables are not of primary interest to be included in a generalizable treatment strategy. Furthermore, there is not enough prior knowledge of the effect of the treatments or of the importance of the covariates for us to explicitly specify the dependency between the outcome and different covariates, thus we choose a model that is flexible enough to accommodate the possibly complex association of the outcome on the covariates. We consider observational studies with a survival outcome and propose to use Random Survival Forest with Weighted Bootstrap (RSFWB) to model the counterfactual outcomes while marginalizing over the auxiliary covariates. By maximizing the restricted mean survival time, we estimate the optimal regime for a target population based on a selected set of covariates. Simulation studies illustrate that the proposed method performs reliably across a range of different scenarios. We further apply RSFWB to a prostate cancer study.