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

作者：Liu Wei^*; Li Shuyou

来源：Journal of Applied Statistics, 2015, 42(3): 463-476.

DOI：10.1080/02664763.2014.960372

• measurement error
• longitudinal data
• missing data
• multiple imputation
• MCMC method

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.

• 出版日期2015-3-4
• 单位辽宁工业大学