Ghosh Pulak, Vaida Florin
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, USA.
Stat Med. 2007 Apr 30;26(9):2074-87. doi: 10.1002/sim.2671.
We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.
我们提出一种变点模型，用于分析接受高效抗逆转录病毒治疗的HIV感染受试者的纵向CD4 T细胞计数。每个受试者的CD4计数曲线遵循一个简单的“折断棍”变点模型，具有随机的受试者特定参数，包括变点。该模型考虑了基线协变量。纵向CD4记录在受试者停止研究治疗时被截尾。这是一种潜在的信息性失访机制，我们通过将其与CD4计数结果联合建模来处理。失访模型纳入了来自CD4模型的项，包括变点。估计在贝叶斯框架下进行，通过WinBUGS软件中的马尔可夫链蒙特卡罗方法实现。使用DIC进行模型选择表明，数据支持复杂的随机变点和信息性截尾模型。
