Missing data strategies for time‐varying confounders in comparative effectiveness studies of non‐missing time‐varying exposures and right‐censored outcomes

The treatment of missing data in comparative effectiveness studies with right‐censored outcomes and time‐varying covariates is challenging because of the multilevel structure of the data. In particular, the performance of an accessible method like multiple imputation (MI) under an imputation model that ignores the multilevel structure is unknown and has not been compared to complete‐case (CC) and single imputation methods that are most commonly applied in this context. Through an extensive simulation study, we compared statistical properties among CC analysis, last value carried forward, mean imputation, the use of missing indicators, and MI‐based approaches with and without auxiliary variables under an extended Cox model when the interest lies in characterizing relationships between non‐missing time‐varying exposures and right‐censored outcomes. MI demonstrated favorable properties under a moderate missing‐at‐random condition (absolute bias