Xu Yaoyao, Yu Menggang, Zhao Ying-Qi, Li Quefeng, Wang Sijian, Shao Jun
Department of Statistics, University of Wisconsin, Madison, Wisconsin, U.S.A.
Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, U.S.A.
Biometrics. 2015 Sep;71(3):645-53. doi: 10.1111/biom.12322. Epub 2015 May 11.
To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.
当治疗效果存在显著异质性时，为便于进行比较性治疗选择，识别出具有不同治疗效果的亚组非常重要。现有方法直接对结果进行建模，然后根据治疗与协变量之间的相互作用来定义亚组。由于结果受到协变量 - 治疗相互作用和协变量主效应的双重影响，直接对结果进行建模可能会因模型设定错误而变得困难，尤其是在存在许多协变量的情况下。另一种方法是直接进行差异治疗效果估计。我们提出了一种方法，该方法近似一个目标函数，其值直接反映对患者的正确治疗分配。该函数使用患者结果作为权重而非建模目标。因此，我们的方法能够以相同方式处理二元、连续、事件发生时间以及可能受污染的结果。我们首先专注于仅从表征重要亚组的线性规则中识别方向性估计。我们进一步考虑对已识别亚组的比较治疗效果进行估计。我们在模拟研究和对两个真实数据集的分析中展示了我们方法的优势。
