Regularized outcome weighted subgroup identification for differential treatment effects.
Regularized outcome weighted subgroup identification for differential treatment effects.
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作者：
Xu Y;Yu M;Zhao YQ;Li Q;Wang S;Shao J
当治疗效果存在实质性异质性时，为了便于比较治疗选择，确定表现出差异治疗效果的亚组是很重要的。现有的方法直接对结果建模，然后根据治疗和协变量之间的相互作用定义亚组。由于结果受到协变量-治疗相互作用和协变量主效应的影响，由于模型规格错误，特别是在存在许多协变量的情况下，直接建模结果可能很困难。或者，可以直接使用差分处理效果估计。我们提出了一种近似目标函数的方法，其值直接反映了患者的正确治疗分配。该函数使用患者结果作为权重，而不是建模目标。因此，我们的方法可以以相同的方式处理二进制、连续、时间到事件以及可能受污染的结果。我们首先专注于从表征重要子群的线性规则中识别方向估计。我们进一步考虑对确定的亚组的比较治疗效果的估计。我们在模拟研究和两个真实数据集的分析中证明了我们的方法的优势。
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.