Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
用边际结构模型估计非随机处理的联合因果效应
Even in the absence of unmeasured confounding factors or model misspecification, standard methods for estimating the causal effect of time-varying treatments on survival are biased when (a) there exists a time-dependent risk factor for survival that also predicts subsequent treatment, and (b) past treatment history predicts subsequent risk factor level. In contrast, methods based on marginal structural models (MSMs) can provide consistent estimates of causal effects when…