Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearners have been proposed recently to estimate CATEs in an effective and flexible way by re-purposing predictive machine learning models for causal estimation. In this chapter, we summarize the literature on metalearners and provide concrete guidance for their application for treatment heterogeneity estimation from randomized controlled trials' data with survival outcomes. The guidance we provide is supported by a comprehensive simulation study in which we vary the complexity of the underlying baseline risk and CATE functions, the magnitude of the heterogeneity in the treatment effect, the censoring mechanism, and the balance in treatment assignment. To demonstrate the applicability of our findings, we reanalyze the data from the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. While recent literature reports the existence of heterogeneous effects of intensive blood pressure treatment with multiple treatment effect modifiers, our results suggest that many of these modifiers may be spurious discoveries. This chapter is accompanied by 'survlearners', an R package that provides well-documented implementations of the CATE estimation strategies described in this work, to allow easy use of our recommendations as well as reproduction of our numerical study.
翻译:对有条件平均治疗效果的估算(CATEs)在现代医学中发挥着至关重要的作用,为患者一级治疗决策提供信息。最近,一些金属制造者提议以有效和灵活的方式估算CATE,重新定位预测机器学习模型,以进行因果关系估计。在本章中,我们总结了金属采集者文献,并提供了具体指导,以根据随机控制试验数据进行治疗异质估计,并得出生存结果。我们提供的指导得到了一项全面模拟研究的支持,在这项研究中,我们改变了基本基线风险和CATE功能的复杂性、治疗效果、检查机制以及治疗任务平衡方面的异质性。为了证明我们的调查结果是否适用,我们重新分析了有关金属采集者文献的文献,并对控制糖尿病中心血管风险行动的研究(ACCORD)中的数据进行了具体指导。虽然最近的文献报告存在密集血压处理和多种治疗效果修饰者的异性效应,但我们的研究结果表明,许多这些修改者可能会对治疗效果、检查机制以及治疗任务分配的异性程度作出误导性评估。这一章节中,我们很好地介绍了我们关于复制战略的实施。这一项研究还附有文件。