A multiple imputation method for missing covariates in non-linear mixed-effects models with application to HIV dynamics.
We propose a three-step multiple imputation method, implemented by Gibbs sampler, for estimating parameters in non-linear mixed-effects models with missing covariates. Estimates obtained by the proposed multiple imputation method are compared to those obtained by the mean-value imputation method and the complete-case method through simulations. We find that the proposed multiple imputation method offers smaller biases and smaller mean-squared errors for the estimates of covariate coefficients compared to other two methods. We apply the three missing data methods to modelling HIV viral dynamics from an AIDS clinical trial. We believe that the results from the proposed multiple imputation method are more reliable than that from the other two commonly used methods.
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Statistical and Data Analysis Center, Harvard School of Public Health, Frontier Science & Technology Research Foundation, Inc., 1244 Boylston Street, Suite 303, Chestnut Hill, Massachusetts 02467, USA. wu@sdac.harvard.edu
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D023241:Antiretroviral Therapy, Highly Active; D002986:Clinical Trials as Topic / Q000379:methods; D003198:Computer Simulation; D015658:HIV Infections / Q000188:drug therapy* / Q000453:epidemiology; D015497:HIV-1 / Q000187:drug effects*; D006801:Humans; D008954:Models, Biological; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D012367:RNA, Viral / Q000097:blood; D019562:Viral Load
D012367:RNA, Viral