Department of Biostatistics, Indiana University School of Medicine, 410 W. 10th Street, Suite 3000, Indianapolis, IN, 46202-3002, USA.
Stat Med. 2013 Mar 15;32(6):1038-53. doi: 10.1002/sim.5557. Epub 2012 Aug 15.
Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes, including biomarker measures, cognitive functions, and clinical symptoms. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. Univariate change point models have been used to model various clinical endpoints, such as CD4 count in studying the progression of HIV infection and cognitive function in the elderly. We propose to use bivariate change point models for two longitudinal outcomes with a focus on the correlation between the two change points. We consider three types of change point models in the bivariate model setting: the broken-stick model, the Bacon-Watts model, and the smooth polynomial model. We adopt a Bayesian approach using a Markov chain Monte Carlo sampling method for parameter estimation and inference. We assess the proposed methods in simulation studies and demonstrate the methodology using data from a longitudinal study of dementia.
流行病学和临床研究通常会收集多个纵向结果的测量值，包括生物标志物测量值、认知功能和临床症状。这些纵向结果可用于确定相关生物过程的时间顺序及其与临床症状发作的关联。单变量变化点模型已被用于对各种临床终点进行建模，例如在研究 HIV 感染进展时的 CD4 计数以及老年人的认知功能。我们建议在具有两个纵向结果的二元模型设置中使用双变量变化点模型，并重点关注两个变化点之间的相关性。我们在二元模型设置中考虑了三种类型的变化点模型：断裂棒模型、培根-沃茨模型和光滑多项式模型。我们采用贝叶斯方法，使用马尔可夫链蒙特卡罗抽样方法进行参数估计和推断。我们在模拟研究中评估了所提出的方法，并使用痴呆症纵向研究的数据来说明该方法。
