Estimating Treatment Effects with Missings Not At Random in the Estimand Framework using Causal Inference

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 missingness process with directed acylcic graphs and patient-specific potential response variables, we present a new approach to obtain an unbiased estimation of treatment effects without needing to impute missing values. Additionally, we provide a formal that the average conditional log-odds ratio is a robust measure even under MNAR missing values if adjusted by sufficient confounders.