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A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values

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  • Wei Liu
  • Shuyou Li

Abstract

In longitudinal studies, nonlinear mixed-effects models have been widely applied to describe the intra- and the inter-subject variations in data. The inter-subject variation usually receives great attention and it may be partially explained by time-dependent covariates. However, some covariates may be measured with substantial errors and may contain missing values. We proposed a multiple imputation method, implemented by a Markov Chain Monte-Carlo method along with Gibbs sampler, to address the covariate measurement errors and missing data in nonlinear mixed-effects models. The multiple imputation method is illustrated in a real data example. Simulation studies show that the multiple imputation method outperforms the commonly used naive methods.

Suggested Citation

  • Wei Liu & Shuyou Li, 2015. "A multiple imputation approach to nonlinear mixed-effects models with covariate measurement errors and missing values," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 463-476, March.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:3:p:463-476
    DOI: 10.1080/02664763.2014.960372
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