Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features

详细信息    查看全文

• 作者：Yangxin Huang ; Tao Lu
• 关键词：AIDS clinical trial ; Bayesian analysis ; PLME models ; Skew ; t distribution ; Viral load and CD4 cell count
• 刊名：Computational Statistics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：32
• 期：1
• 页码：179-196
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods;
• 出版者：Springer Berlin Heidelberg
• ISSN：1613-9658
• 卷排序：32

文摘

The relationship between viral load and CD4 cell count is one of the interesting questions in AIDS research. Statistical models are powerful tools for clarifying this important problem. Partially linear mixed-effects (PLME) model which accounts for the unknown function of time effect is one of the important models for this purpose. Meanwhile, the mixed-effects modeling approach is suitable for the longitudinal data analysis. However, the complex process of data collection in clinical trials has made it impossible to rely on one particular model to address the issues. Asymmetric distribution, measurement error and left censoring are features commonly arisen in longitudinal studies. It is crucial to take into account these features in the modeling process to achieve reliable estimation and valid conclusion. In this article, we establish a joint model that accounts for all these features in the framework of PLME models. A Bayesian inferential procedure is proposed to estimate parameters in the joint model. A real data example is analyzed to demonstrate the proposed modeling approach for inference and the results are reported by comparing various scenarios-based models.