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.
翻译:评估观察数据中不同处理效果的方法,主要侧重于连续或二进制结果,相对而言,对生存结果的审查较少。在反事实框架内使用灵活的机器学习方法,是应对因复杂个人特点而出现的挑战的一个很有希望的方法,需要为此制定相应的治疗方法。为了评估最近用于估计治疗效果的离异性并了解更好的做法,我们进行了一项全面模拟研究,展示了各种描述复杂不同生存治疗效果和不同程度的共变重叠的广泛环境。我们的研究结果表明,在加速失败时间模型(AFT-BART-NP)框架内的非对称巴伊西亚安地基亚基亚基亚基亚基亚基亚基亚基亚回归树作为加速失败时间模型(AFT-BART-NP)框架内的额外固定变量,在偏差、精确和预期遗憾方面,始终取得最佳的成绩。此外,AFT-BART-BART-NP的可靠间隔估计方法为在变异性治疗效果至少为中度重叠的情况下,个人生存治疗效果的表面常态覆盖范围。在AFT-BART-NPS-NP模型制定中,我们通过不断进行快速研究的不断的核化结果风险风险研究,可以进一步展示。