Estimating heterogeneous survival treatment effect in observational data using machine learning

TL;DR

This paper evaluates machine learning methods for estimating individual survival treatment effects in observational data, highlighting the superior performance of AFT-BART-NP in various simulation settings and a real case study.

Contribution

It provides a comprehensive simulation comparison of survival treatment effect estimators and demonstrates the effectiveness of AFT-BART-NP with practical applications.

Findings

AFT-BART-NP consistently outperforms other methods in bias and precision.

Credible intervals from AFT-BART-NP achieve near-nominal coverage under moderate covariate overlap.

Including estimated propensity scores improves AFT-BART-NP efficiency and coverage.

Abstract

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.