Simultaneous inference for semiparametric nonlinear mixed-effects models with covariate measurement errors and missing responses.
Semiparametric nonlinear mixed-effects (NLME) models are flexible for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain interindividual variations. Some covariates, however, may be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. We propose two approximate likelihood methods for semiparametric NLME models with covariate measurement errors and nonignorable missing responses. The methods are illustrated in a real data example. Simulation results show that both methods perform well and are much better than the commonly used naive method.
Liu
Wei
W
Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada. wei@stat.ubc.ca
Wu
Lang
L
D000163:Acquired Immunodeficiency Syndrome / Q000276:immunology / Q000821:virology; D000465:Algorithms; D000704:Analysis of Variance; D001699:Biometry; D018791:CD4 Lymphocyte Count; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics