期刊
STATISTICS IN MEDICINE
卷 36, 期 12, 页码 1884-1894出版社
WILEY
DOI: 10.1002/sim.7240
关键词
Cholesky decomposition; generalized partially linear model; joint mean-covariance model; longitudinal proportional data; quality of life
类别
资金
- Youth National Nature and Science Foundation of China [11501124]
- National Nature and Science Foundation of China [11371100]
- Queen's Research Opportunities Funds
- Natural Sciences and Engineering Research Council of Canada
Motivated by the analysis of quality of life data from a clinical trial on early breast cancer, we propose in this paper a generalized partially linear mean-covariance regression model for longitudinal proportional data, which are bounded in a closed interval. Cholesky decomposition of the covariance matrix for within-subject responses and generalized estimation equations are used to estimate unknown parameters and the nonlinear function in the model. Simulation studies are performed to evaluate the performance of the proposed estimation procedures. Our new model is also applied to analyze the data from the cancer clinical trial that motivated this research. In comparison with available models in the literature, the proposed model does not require specific parametric assumptions on the density function of the longitudinal responses and the probability function of the boundary values and can capture dynamic changes of time or other interested variables on both mean and covariance of the correlated proportional responses. Copyright (c) 2017 John Wiley & Sons, Ltd.
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