title: Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data.
abstract: In many longitudinal studies, the individual characteristics associated with the repeated measures may be possible covariates of the time to an event of interest, and thus, it is desirable to model the time-to-event process and the longitudinal process jointly. Statistical analyses may be further complicated in such studies with missing data such as informative dropouts. This article considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the time-to-event process. We provide a method for simultaneous likelihood inference on the 2 models and allow for nonignorable data missing. The approach is illustrated with a recent AIDS study by jointly modeling HIV viral dynamics and time to viral rebound.
authors.affiliation: Department of Statistics, 333-6356 Agricultural road, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada. lang@stat.ubc.ca
authors.forename: Lang
authors.initials: L
authors.lastname: Wu
authors.forename: X Joan
authors.initials: XJ
authors.lastname: Hu
authors.forename: Hulin
authors.initials: H
authors.lastname: Wu
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000188:drug therapy / Q000453:epidemiology*; D019380:Anti-HIV Agents / Q000627:therapeutic use; D001699:Biometry / Q000379:methods; D018450:Disease Progression; D006678:HIV / Q000472:pathogenicity; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012016:Reference Values; D012107:Research Design / Q000706:statistics & numerical data; D013995:Time; D016896:Treatment Outcome; D019562:Viral Load / Q000706:statistics & numerical data
chemical_list: D019380:Anti-HIV Agents