An approximate method for generalized linear and nonlinear mixed effects models with a mechanistic nonlinear covariate measurement error model

Hongbin Zhang, Lang Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The literature on measurement error for time-dependent covariates has mostly focused on empirical models, such as linear mixed effects models. Motivated by an AIDS study, we propose a joint modeling method in which a mechanistic nonlinear model is used to address the time-varying covariate measurement error for a longitudinal outcome that can be either discrete such as binary and count or continuous. We implement an inference procedure that uses first-order Taylor approximation to linearize both the covariate model and the response model. We study the asymptotic properties of the joint model based estimator and provide proof of consistency and normality. We then evaluate the performance of estimation in finite sample size scenario through simulation. Finally, we apply the new method to real data in an HIV/AIDS study.

Original languageEnglish
Pages (from-to)471-499
Number of pages29
JournalMetrika
Volume82
Issue number4
DOIs
StatePublished - May 1 2019

Bibliographical note

Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Asymptotic
  • First-order Taylor approximation
  • Longitudinal data
  • Measurement error
  • Nonlinear covariate model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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