Missing data strategies for time‐varying confounders in comparative effectiveness studies of non‐missing time‐varying exposures and right‐censored outcomes. (17th May 2019)

• Record Type: Journal Article
• Authors: Desai, Manisha; Montez‐Rath, Maria E.; Kapphahn, Kristopher; Joyce, Vilija R.; Mathur, Maya B.; Garcia, Ariadna; Purington, Natasha; Owens, Douglas K.
• Is Part Of: Statistics in medicine. Volume 38:Number 17(2019)
• Page Start: 3204
• Page End: 3220
• Publication Date: 2019-05-17
• Subjects: missing data -- multilevel data -- multiple imputation -- right‐censored outcome -- time‐varying covariates; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8174
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : 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 <0.1) and outperformed CC and single imputation methods, even when the MI method did not account for correlated observations in the imputation model. The performance of MI decreased with increasing complexity such as when the missing data mechanism involved the exposure of interest, but was still preferred over other methods considered and performed well in the presence of strong auxiliary variables. We recommend considering MI that ignores the multilevel structure in the imputation model when data are missing in a time‐varying confounder, incorporating variables associated with missingness in the MI models as well as conducting sensitivity analyses across plausible assumptions.