Participant noncompliance, in which participants do not follow their assigned treatment protocol, often obscures the causal relationship between treatment and treatment effect in randomized trials. In the longitudinal setting, the G-computation algorithm can adjust for confounding to estimate causal effects. Typically, G-computation assumes that both 1) compliance is observed; and 2) the densities of the confounders can be correctly specified. We aim to develop a G-computation estimator in the setting where both assumptions are violated. For 1), in place of unobserved compliance, we substitute in probability weights derived from modeling a biomarker associated with compliance. For 2), we fit semiparametric models using predictive mean matching. Specifically, we parametrically specify only the conditional mean of the confounders, and then use predictive mean matching to randomly generate confounder data for G-computation. In both the simulation and application, we compare multiple causal estimators already established in the literature with those derived from our method. For the simulation, we generated data across different sample sizes and levels of confounding. For the application, we apply our method to a trial that sought to evaluate the effect of cigarettes with low nicotine on cigarette consumption (Center for the Evaluation of Nicotine in Cigarettes Project 2 - CENIC-P2).

翻译：参与者的不依从性（即未遵循指定治疗方案）常会模糊随机试验中处理与处理效应之间的因果关系。在纵向设定下，G计算算法可通过调整混杂因素来估计因果效应。标准的G计算算法假设：1）依从性可被观测；2）混杂因素的密度函数可被正确设定。我们旨在开发一种在两项假设均被违反情形下适用的G计算估计量。针对假设1），我们用通过建模与依从性相关的生物标志物导出的概率权重替代未观测的依从性；针对假设2），我们采用预测均值匹配拟合半参数模型——仅参数化设定混杂因素的条件均值，继而通过预测均值匹配随机生成用于G计算的混杂因素数据。在模拟和实际应用中，我们将文献中已建立的多个因果估计量与基于本文方法导出的估计量进行对比。模拟实验在不同样本量和混杂强度下生成数据；实际应用则将其应用于一项评估低尼古丁卷烟对卷烟消费量影响的试验（美国尼古丁卷烟评估中心项目2——CENIC-P2）。