Ganjali M, Baghfalaki T
a Department of Statistics , Shahid Beheshti University , Tehran , Iran.
J Biopharm Stat. 2015;25(5):1077-99. doi: 10.1080/10543406.2014.971584. Epub 2014 Nov 5.
Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.
纵向测量数据和事件发生时间数据的联合建模通常通过拟合共享参数模型来进行。另一种可用于联合建模的方法是边际模型。作为一种边际模型，我们使用高斯模型对纵向测量数据和事件发生时间数据进行联合建模。我们考虑用于纵向数据建模的回归模型和用于事件时间数据建模的威布尔比例风险模型。使用高斯copula来考虑这两个模型之间的关联。采用蒙特卡罗期望最大化方法进行参数估计。进行了一些模拟研究以说明所提出的方法。此外，所提出的方法用于分析一个临床试验数据集。
