Estimating Treatment Effects with Missings Not At Random in the Estimand Framework using Causal Inference
估计目标框架下非随机缺失的处理效应估计
The analysis of randomized trials is often complicated by the occurrence of intercurrent events and missing values. Even though there are different strategies to address missing values it is still common to require missing values imputation. In the present article we explore the estimation of treatment effects in RCTs from a causal inference perspective under different missing data mechanisms with a particular emphasis on missings not at random (MNAR). By modelling the…