Liu Xuefeng, Daniels Michael J, Marcus Bess
Department of Biostatistics and Epidemiology, East Tennessee State University, Johnson City, TN 37614.
J Am Stat Assoc. 2009 Jun 1;104(486):429-438. doi: 10.1198/016214508000000904.
Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.
针对纵向二元过程和纵向连续过程之间的关联直接受到关注的情况，提出了联合模型。对模型进行参数化，使得两个过程之间的依赖关系由无约束回归系数来表征。使用贝叶斯变量选择技术对这些系数进行简约建模。开发了一种马尔可夫链蒙特卡罗（MCMC）采样算法，用于从后验分布中采样，并使用数据扩充步骤来处理缺失数据。为有效实施MCMC算法，解决了几个技术问题。这些模型的提出源于一项戒烟临床试验的分析，并用于该分析，在该试验中，一个重要的关注点是（运动）治疗对戒烟与体重增加之间关系的影响。
