Bibliographic Details
| Title: |
Bayesian joint analysis of heterogeneous- and skewed-longitudinal data and a binary outcome, with application to AIDS clinical studies. |
| Authors: |
Xiaosun Lu, Yangxin Huang, Jiaqing Chen, Rong Zhou, Shuli Yu, Ping Yin, Lu, Xiaosun, Huang, Yangxin, Chen, Jiaqing, Zhou, Rong, Yu, Shuli, Yin, Ping |
| Source: |
Statistical Methods in Medical Research; Oct2018, Vol. 27 Issue 10, p2946-2963, 18p |
| Subject Terms: |
SKEWNESS (Probability theory), BAYESIAN analysis, LONGITUDINAL method, FINITE mixture models (Statistics), ANTIRETROVIRAL agents |
| Abstract: |
In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome. In this article, we propose a joint model in which a flexible finite mixture of nonlinear mixed-effects models with skew distributions is connected with binary logistic model by a latent class membership indicator. Simulation studies are conducted to assess the performance of the proposed models and method, and a real example from an AIDS clinical trial study illustrates the methodology by modeling the viral dynamics to compare potential models with different distribution specifications; the analysis results are reported. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
