title: A generalized partially linear mean-covariance regression model for longitudinal proportional data, with applications to the analysis of quality of life data from cancer clinical trials.
abstract: 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 © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, 200032, China.
authors.forename: Xueying
authors.initials: X
authors.lastname: Zheng
authors.affiliation: Department of Biostatistics, School of Public Health, Key Laboratory of Public Health Safety and Collaborative Innovation Center of Social Risks Governance in Health, Fudan University, Shanghai, 200032, China.
authors.forename: Guoyou
authors.initials: G
authors.lastname: Qin
authors.affiliation: Canadian Cancer Trials Group, Queen's University, Kingston, Ontario, Canada.|Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.
authors.forename: Dongsheng
authors.identifier: 0000-0003-4842-2184
authors.initials: D
authors.lastname: Tu
mesh_terms: D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D009369:Neoplasms / Q000523:psychology / Q000628:therapy*; D017711:Nonlinear Dynamics; D016016:Proportional Hazards Models; D011788:Quality of Life / Q000523:psychology; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012044:Regression Analysis
keywords: Cholesky decomposition; generalized partially linear model; joint mean-covariance model; longitudinal proportional data; quality of life