论文标题
双重稳健的自适应拉索，用于效果修饰符发现
Doubly Robust Adaptive LASSO for Effect Modifier Discovery
论文作者
论文摘要
当治疗对结果的影响因第三个变量的水平而异（效应修饰符，EM）时，就会发生效应修饰。评估效应修饰的一种自然方法是通过亚组分析或在结果回归中包括治疗与协变量之间的相互作用项。但是，除非指定了正确指定的结果模型，否则后者不会针对边缘结构模型（MSM）的参数。我们的目的是开发一种数据自适应方法，以选择单个时间点暴露的MSM中的效果修改变量。提出了两阶段的程序。首先，我们估计有条件的结果期望和倾向得分，并将其插入双重强大的损失函数。其次，我们使用自适应拉索选择EMS并估计MSM系数。然后，选择后推理用于获得所选EMS的覆盖范围。进行仿真研究以验证所提出的方法的性能。
Effect modification occurs when the effect of the treatment on an outcome differs according to the level of a third variable (the effect modifier, EM). A natural way to assess effect modification is by subgroup analysis or include the interaction terms between the treatment and the covariates in an outcome regression. The latter, however, does not target a parameter of a marginal structural model (MSM) unless a correctly specified outcome model is specified. Our aim is to develop a data-adaptive method to select effect modifying variables in an MSM with a single time point exposure. A two-stage procedure is proposed. First, we estimate the conditional outcome expectation and propensity score and plug these into a doubly robust loss function. Second, we use the adaptive LASSO to select the EMs and estimate MSM coefficients. Post-selection inference is then used to obtain coverage on the selected EMs. Simulations studies are performed in order to verify the performance of the proposed methods.
