We propose to use the difference in natural parameters (DINA) to quantify the heterogeneous treatment effect for the exponential family, a.k.a. the hazard ratio for the Cox model, in contrast to the difference in means. For responses such as binary outcome and survival time, DINA is of more practical interest and convenient for modeling the covariates' influences on the treatment effect. We introduce a DINA estimator that is insensitive to confounding and non-collapsibility issues, and allows practitioners to use powerful off-the-shelf machine learning tools for nuisance estimation. We use extensive simulations to demonstrate the efficacy of the proposed method with various response distributions and censoring mechanisms. We also apply the proposed method to the SPRINT dataset to estimate the heterogeneous treatment effect, testify the method's robustness to nuisance estimation, and conduct placebo evaluation.
翻译:我们建议使用自然参数差异(DINA)来量化指数式家庭(a.k.a.)的多种治疗效果(DINA),以量化Cox模型的危险比率(与手段上的差异形成对比),对于二进制结果和生存时间等应对措施,DINA更符合实际利益,更方便地模拟共变效应对治疗效果的影响。我们引入了DINA估算器,该估算器对混杂和非重叠问题不敏感,并允许从业人员使用强大的现成机器学习工具来进行骚扰估计。我们使用广泛的模拟来展示拟议方法的效力,同时使用各种响应分布和审查机制。我们还将拟议方法应用于SPRINT数据集,以估计不同治疗效果,证明该方法对骚扰估计的稳健性,并进行安慰评估。