Abstract
We develop a novel doubly-robust (DR) imputation framework for longitudinal studies with monotone dropout, motivated by the informative dropout that is common in FDA-regulated trials for Alzheimer’s disease. In this approach the missing data are first imputed using a doubly-robust augmented inverse probability weighting (AIPW) estimator; then the imputed completed data are substituted into a full-data estimating equation, and the estimate is obtained using standard software. The imputed completed data may be inspected and compared to the observed data, and standard model diagnostics are available. The same imputed completed data can be used for several different estimands, such as subgroup analyses in a clinical trial, allowing for reduced computation and increased consistency across analyses. We present two specific DR imputation estimators, AIPW-I and AIPW-S, study their theoretical properties, and investigate their performance by simulation. AIPW-S has substantially reduced computational burden, compared to many other DR estimators, at the cost of some loss of efficiency and the requirement of stronger assumptions. Simulation studies support the theoretical properties and good performance of the DR imputation framework. Importantly, we demonstrate their ability to address time-varying covariates, such as a time by treatment interaction. We illustrate using data from a large randomized Phase III trial, investigating the effect of donepezil in Alzheimer’s disease, from the Alzheimer’s Disease Cooperative Study (ADCS) group.
Funding Statement
The authors gratefully acknowledge support from grant number R01AG061146 from the US DHHS NIH National Institute on Aging, and from grant number P30 CA023100 from the NIH National Cancer Institute.
Data collection and sharing for this project were obtained from the Alzheimer’s Disease Cooperative Study (ADCS), funded by the National Institutes of Health Grant U19 AG010483.
Acknowledgments
The authors gratefully acknowledge Dr. Howard Feldman, PI of the Alzheimer’s Disease Cooperative Study, for his mentorship and support of the first author during his Ph.D. studies at University of California San Diego.
The authors also thank the Editor, Jeffrey Morris, the corresponding Associate Editor, and two referees for insightful comments which led to important improvements over the earlier drafts.
Citation
Yuqi Qiu. Karen Messer. "An efficient doubly-robust imputation framework for longitudinal dropout, with an application to an Alzheimer’s clinical trial." Ann. Appl. Stat. 17 (3) 2473 - 2493, September 2023. https://doi.org/10.1214/23-AOAS1728
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