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

SUBMITTER: Hu L 

PROVIDER: S-EPMC9827499 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature