Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses

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Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses

TitleSimultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses
Publication TypeJournal Article
Year of Publication2007
AuthorsLiu, W, WU, LANG
JournalBiometrics
Volume63
Pagination342–350
Date Publishedjun
ISSN1541-0420
KeywordsCubic spline basis, Longitudinal data, Monte Carlo EM algorithm, Random-effects model
AbstractSummary 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.
URLhttp://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2006.00687.x/abstract
DOI10.1111/j.1541-0420.2006.00687.x