  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 treatment effect heterogeneity and inform better
practice, we carry out a comprehensive simulation study presenting a wide range
of settings describing confounded heterogeneous survival treatment effects and
varying degrees of covariate overlap. Our results suggest that the
nonparametric Bayesian Additive Regression Trees within the framework of
accelerated failure time model (AFT-BART-NP) consistently yields the best
performance, in terms of bias, precision and expected regret. 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. Including a non-parametrically
estimated propensity score as an additional fixed covariate in the AFT-BART-NP
model formulation can further improve its efficiency and frequentist coverage.
Finally, we demonstrate the application of flexible causal machine learning
estimators through a comprehensive case study examining the heterogeneous
survival effects of two radiotherapy approaches for localized high-risk
prostate cancer.
