Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies

Tsung I. Lin, Wan Lun Wang

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

The analysis of multivariate longitudinal data could encounter some complications due to censorship induced by detection limits of the assay and nonresponse occurring when participants missed scheduled visits intermittently or discontinued participation. This paper establishes a generalization of the multivariate linear mixed model that can accommodate censored responses and nonignorable missing outcomes simultaneously. To account for the nonignorable missingness, the selection approach which decomposes the joint distribution as a marginal distribution for the primary outcome variables and a model describing the missing process conditional on the hypothetical complete data is used. A computationally feasible Monte Carlo expectation conditional maximization algorithm is developed for parameter estimation with the maximum likelihood (ML) method. Furthermore, a general information-based approach is presented to assess the variability of ML estimators. The techniques for the prediction of censored responses and imputation of missing outcomes are also discussed. The methodology is motivated and exemplified by a real dataset concerning HIV-AIDS clinical trials. A simulation study is conducted to examine the performance of the proposed method compared with other traditional approaches.

原文English
頁(從 - 到)1325-1339
頁數15
期刊Biometrical Journal
64
發行號7
DOIs
出版狀態Accepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 統計、概率和不確定性

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