A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness

JOURNAL OF MULTIVARIATE ANALYSIS

卷:117

Article

Chen, Baojiang¹  Zhou, Xiao-Hua² 

[1] Univ Nebraska Med Ctr, Dept Biostat, Omaha, NE 68198 USA

[2] Univ Washington, Dept Biostat, Seattle, WA 98195 USA

关键词: Cluster;    Missing not at random;    Markov non-homogeneous;    Random effects;    Transition intensity;   

DOI  :  10.1016/j.jmva.2013.01.009

来源: Elsevier

【 摘 要 】

Life history data arising in clusters with pre-specified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous process. In this paper we develop methods to deal with non-homogeneous Markov process with incomplete clustered life history data. A correlated random effects model is developed to deal with the nonignorable missingness, and a time transformation is employed to address the non-homogeneity in the transition model. Maximum likelihood estimate based on the Monte-Carlo EM algorithm is advocated for parameter estimation. Simulation studies demonstrate that the proposed method works well in many situations. We also apply this method to an Alzheimer's disease study. (C) 2013 Elsevier Inc. All rights reserved.

【 授权许可】

Free