Dataset for: 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

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 which motivated this study. 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 responses.

本研究受早期乳腺癌临床试验的生活质量数据分析启发，针对闭区间内有界的纵向比例数据，本文提出一种广义部分线性均值-协方差回归模型。我们采用受试者内响应协方差矩阵的乔列斯基分解（Cholesky decomposition）与广义估计方程（Generalized Estimating Equations），对模型中的未知参数与非线性函数进行估计。通过模拟研究评估所提估计方法的性能。我们还将所提出的新模型应用于启发本研究的乳腺癌临床试验数据的分析。与现有文献中的模型相比，所提模型无需对纵向响应的密度函数及边界值的概率函数施加特定参数假设，且能够捕捉时间或其他关注变量对相关响应的均值与协方差的动态变化。