title: Joint multiple imputation for longitudinal outcomes and clinical events that truncate longitudinal follow-up.
abstract: Longitudinal cohort studies often collect both repeated measurements of longitudinal outcomes and times to clinical events whose occurrence precludes further longitudinal measurements. Although joint modeling of the clinical events and the longitudinal data can be used to provide valid statistical inference for target estimands in certain contexts, the application of joint models in medical literature is currently rather restricted because of the complexity of the joint models and the intensive computation involved. We propose a multiple imputation approach to jointly impute missing data of both the longitudinal and clinical event outcomes. With complete imputed datasets, analysts are then able to use simple and transparent statistical methods and standard statistical software to perform various analyses without dealing with the complications of missing data and joint modeling. We show that the proposed multiple imputation approach is flexible and easy to implement in practice. Numerical results are also provided to demonstrate its performance. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, U.S.A.
authors.forename: Bo
authors.initials: B
authors.lastname: Hu
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A.
authors.forename: Liang
authors.initials: L
authors.lastname: Li
authors.affiliation: Division of Clinical Epidemiology, University of Utah, Salt Lake City, UT, U.S.A.
authors.forename: Tom
authors.initials: T
authors.lastname: Greene
mesh_terms: D001699:Biometry; D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical
keywords: competing risk; joint modeling; longitudinal data; missing data; multiple imputation