Baghfalaki Taban, Ganjali Mojtaba, Berridge Damon
Department of Statistics, Shahid Beheshti University, Tehran, 1983963113, Iran.
Biom J. 2013 Nov;55(6):844-65. doi: 10.1002/bimj.201200272. Epub 2013 Aug 1.
Joint modeling of longitudinal data and survival data has been used widely for analyzing AIDS clinical trials, where a biological marker such as CD4 count measurement can be an important predictor of survival. In most of these studies, a normal distribution is used for modeling longitudinal responses, which leads to vulnerable inference in the presence of outliers in longitudinal measurements. Powerful distributions for robust analysis are normal/independent distributions, which include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions in addition to the normal. In this paper, a linear-mixed effects model with normal/independent distribution for both random effects and residuals and Cox's model for survival time are used. For estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. Some simulation studies are performed for illustration of the proposed method. Also, the method is illustrated on a real AIDS data set and the best model is selected using some criteria.
纵向数据和生存数据的联合建模已被广泛用于分析艾滋病临床试验，其中诸如CD4计数测量之类的生物标志物可能是生存的重要预测指标。在大多数此类研究中，使用正态分布对纵向反应进行建模，这在纵向测量存在异常值的情况下会导致脆弱的推断。用于稳健分析的强大分布是正态/独立分布，除了正态分布外，还包括单变量和多变量版本的学生t分布、斜线分布和污染正态分布。本文使用了一个随机效应和残差均为正态/独立分布的线性混合效应模型以及生存时间的Cox模型。对于估计，采用了使用马尔可夫链蒙特卡罗的贝叶斯方法。进行了一些模拟研究以说明所提出的方法。此外，该方法在一个真实的艾滋病数据集上进行了说明，并使用一些标准选择了最佳模型。
