Missing covariates in longitudinal data with informative dropouts: bias analysis and inference.
We consider estimation in generalized linear mixed models (GLMM) for longitudinal data with informative dropouts. At the time a unit drops out, time-varying covariates are often unobserved in addition to the missing outcome. However, existing informative dropout models typically require covariates to be completely observed. This assumption is not realistic in the presence of time-varying covariates. In this article, we first study the asymptotic bias that would result from applying existing methods, where missing time-varying covariates are handled using naive approaches, which include: (1) using only baseline values; (2) carrying forward the last observation; and (3) assuming the missing data are ignorable. Our asymptotic bias analysis shows that these naive approaches yield inconsistent estimators of model parameters. We next propose a selection/transition model that allows covariates to be missing in addition to the outcome variable at the time of dropout. The EM algorithm is used for inference in the proposed model. Data from a longitudinal study of human immunodeficiency virus (HIV)-infected women are used to illustrate the methodology.
Roy
Jason
J
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York 14642, USA. jason_roy@urmc.rochester.edu
Lin
Xihong
X
D000465:Algorithms; D015982:Bias; D018791:CD4 Lymphocyte Count; D003198:Computer Simulation; D005260:Female; D015658:HIV Infections / Q000188:drug therapy; D017320:HIV Protease Inhibitors / Q000627:therapeutic use; D006760:Hospitalization; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D010352:Patient Dropouts
D017320:HIV Protease Inhibitors