![Sample our Bioscience journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5925/TOC-Subject-Sample-2021-Bioscience.jpg"})
ABSTRACT
It is well known that there is strong relationship between HIV viral load and CD4 cell counts in AIDS studies. However, the relationship between them changes during the course of treatment and may vary among individuals. During treatments, some individuals may experience terminal events such as death. Because the terminal event may be related to the individual’s viral load measurements, the terminal mechanism is non-ignorable. Furthermore, there exists competing risks from multiple types of events, such as AIDS-related death and other death. Most joint models for the analysis of longitudinal-survival data developed in literatures have focused on constant coefficients and assume symmetric distribution for the endpoints, which does not meet the needs for investigating the nature of varying relationship between HIV viral load and CD4 cell counts in practice. We develop a mixed-effects varying-coefficient model with skewed distribution coupled with cause-specific varying-coefficient hazard model with random-effects to deal with varying relationship between the two endpoints for longitudinal-competing risks survival data. A fully Bayesian inference procedure is established to estimate parameters in the joint model. The proposed method is applied to a multicenter AIDS cohort study. Various scenarios-based potential models that account for partial data features are compared. Some interesting findings are presented.
Acknowledgments
We would like to thank the editor, associate editor, and referees whose valuable suggestions led to major improvements in the overall clarity and presentation of this article.
Funding
The work was partially supported by the Natural Science Foundation of China (grant number 11501503), Natural Science Foundation of Jiangsu Province of China (grant number BK20131340), Social Science Foundation of Chinese Ministry of Education (grant number 12YJCZH128), China Postdoctoral Science Foundation (grant number 2014M560471), Postdoctoral Science Foundation of Zhejiang Province of China (grant number BSH1402026), QingLan Project of Jiangsu Province of China, Priority Academic Program Development of Jiangsu Higher Education Institutions (Applied Economics), and Key Laboratory of Jiangsu Province (Financial Engineering Laboratory).