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A multiple imputation method for missing covariates in non‐linear mixed‐effects models with application to HIV dynamics
Author(s) -
Wu Hulin,
Wu Lang
Publication year - 2001
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.816
Subject(s) - covariate , imputation (statistics) , missing data , statistics , computer science , human immunodeficiency virus (hiv) , econometrics , mathematics , medicine , family medicine
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. Copyright © 2001 John Wiley & Sons, Ltd.