Division of Biostatistics, University of Texas School of Public Health, 1200 Pressler St, Houston, Texas 77030, U.S.A.
Stat Med. 2013 Dec 20;32(29):5133-44. doi: 10.1002/sim.5906. Epub 2013 Aug 2.
This article proposes a joint modeling framework for longitudinal insomnia measurements and a stochastic smoking cessation process in the presence of a latent permanent quitting state (i.e., 'cure'). We use a generalized linear mixed-effects model and a stochastic mixed-effects model for the longitudinal measurements of insomnia symptom and for the smoking cessation process, respectively. We link these two models together via the latent random effects. We develop a Bayesian framework and Markov Chain Monte Carlo algorithm to obtain the parameter estimates. We formulate and compute the likelihood functions involving time-dependent covariates. We explore the within-subject correlation between insomnia and smoking processes. We apply the proposed methodology to simulation studies and the motivating dataset, that is, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large longitudinal cohort study of smokers from Finland.
本文提出了一个联合建模框架，用于在潜在的永久性戒烟状态（即“治愈”）存在的情况下对纵向失眠测量和随机戒烟过程进行建模。我们分别使用广义线性混合效应模型和随机混合效应模型来对失眠症状的纵向测量和戒烟过程进行建模。我们通过潜在随机效应将这两个模型联系在一起。我们建立了一个贝叶斯框架和马尔可夫链蒙特卡罗算法来获得参数估计。我们制定并计算了涉及时变协变量的似然函数。我们探讨了失眠和吸烟过程之间的个体内相关性。我们将所提出的方法应用于模拟研究和动机数据集，即芬兰吸烟者的大型纵向队列研究——α-生育酚、β-胡萝卜素肺癌预防研究。
