Estimating heterogeneous survival treatment effect in observational data using machine learning

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of TEH and inform better practice, we carry out a comprehensive simulation study representing a variety of confounded heterogeneous survival treatment effect settings and varying degrees of covariate overlap. Our results indicate that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently carries the best performance, both in terms of bias and precision. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Under lack of overlap, where accurate estimation of the average treatment effect becomes challenging, the credible intervals from AFT-BART-NP still provide nominal frequentist coverage among units near the centroid of the propensity score distribution. Finally, we demonstrate the application of these machine learning methods through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.