Estimation of Heterogeneous Restricted Mean Survival Time Using Random Forest - Liu, Mingyang; Li, Hongzhe (Frontiers in Genetics, 2021)
Estimation and prediction of heterogeneous restricted mean survival time (hRMST) is of great clinical importance, providing an easily interpretable and clinically meaningful summary of the survival function in the presence of censoring and individual covariates. Existing methods for modeling of the RMST rely on proportional hazards or other parametric assumptions on the survival distribution. In this paper, we propose a random forest based estimation of hRMST for right-censored survival data with covariates and prove a central limit theorem for the resulting estimator. In addition, we present a computationally efficient construction for confidence interval of the hRMST. Our simulations show the resulting confidence intervals have the correct coverage probability of the hRMST and the random forest-based estimate of the hRMST has smaller prediction errors than the parametric models when the models are misspecified. We apply the method to the ovarian cancer data set from The Cancer Genome Atlas (TCGA) project to predict hRMST and show an improved prediction performance over existing methods. A software implementation, srf using R and C++, is available from https://github.com/lmy1019/SRF.