Estimating heterogeneous survival treatment effect in observational data using machine learning

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.

arXiv comment23 pages, 5 figures, 3 tables

References in corpus (9)

• Random survival forests
• DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
• BART: Bayesian additive regression trees
• Balancing Covariates via Propensity Score Weighting
• Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalho
• Bayesian additive regression trees and the General BART model
• Estimating heterogeneous survival treatment effect in observational data using machine learning
• Balancing Covariates via Propensity Score Weighting
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Cited by in corpus (6)

• Estimating heterogeneous survival treatment effect in observational data using machine learning
• A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial
• Enabling Counterfactual Survival Analysis with Balanced Representations
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
• A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
• Horseshoe Forests for High-Dimensional Causal Survival Analysis