Bivariate modelling of longitudinal measurements of two human immunodeficiency type 1 disease progression markers in the presence of informative drop-outs
Bivariate modelling of longitudinal measurements of two human immunodeficiency type 1 disease progression markers in the presence of informative drop-outs
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DOI：
10.1111/j.1467-9876.2005.00491.x
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发表时间：
2005-01-01
影响因子：
1.6
通讯作者：
Touloumi, G
中科院分区：
文献类型：
--
作者：
Pantazis, N;Touloumi, G
在许多涉及替代标志物重复测量的流行病学研究中，主要的统计学问题是经常出现缺失数据。当数据不可忽视地缺失时，标准的基于似然的方法，如线性随机效应模型，不能给出无偏估计。在人类免疫缺陷病毒(HIV)1型感染中，被广泛用于跟踪疾病进展的两个标志物是CD4细胞计数和HIV-核糖核酸(RNA)病毒载量水平。这些标记的重复测量往往会被信息审查，这是不可忽视的遗漏的特例。在这种情况下，我们需要应用对观测数据和缺失过程进行联合建模的方法。尽管它们具有很高的相关性，但这些标记的纵向数据主要是通过随机效应模型进行独立分析的。Toulourni和他的同事提出了一个名为联合多变量随机效应模型的模型，该模型结合了标记潜在模式的线性随机效应模型和退出过程的对数正态生存模型。我们扩展了联合多变量随机效应模型，以同时对CD4细胞和病毒载量数据进行建模，同时调整由于疾病进展或死亡而导致的信息性丢失。在截尾生存数据的情况下，考虑HIV-RNA趋势的非线性，利用EM算法作为嵌套算法，使用约束迭代广义最小二乘法或其改进版本来估计模型的所有参数。在仿真研究中，对所提出的方法进行了评估，并与简单的方法进行了比较。最后，将该方法应用于“欧洲艾滋病和死亡的血清转换协同行动”研究的数据子集。
The main statistical problem in many epidemiological studies which involve repeated measurements of surrogate markers is the frequent occurrence of missing data. Standard likelihood-based approaches like the linear random-effects model fail to give unbiased estimates when data are non-ignorably missing. In human immunodeficiency virus (HIV) type 1 infection, two markers which have been widely used to track progression of the disease are CD4 cell counts and HIV-ribonucleic acid (RNA) viral load levels. Repeated measurements of these markers tend to be informatively censored, which is a special case of non-ignorable missingness. In such cases, we need to apply methods that jointly model the observed data and the missingness process. Despite their high correlation, longitudinal data of these markers have been analysed independently by using mainly random-effects models. Toulourni and co-workers have proposed a model termed the joint multivariate random-effects model which combines a linear random-effects model for the underlying pattern of the marker with a log-normal survival model for the drop-out process. We extend the joint multivariate random-effects model to model simultaneously the CD4 cell and viral load data while adjusting for informative drop-outs due to disease progression or death. Estimates of all the model's parameters are obtained by using the restricted iterative generalized least squares method or a modified version of it using the EM algorithm as a nested algorithm in the case of censored survival data taking also into account non-linearity in the HIV-RNA trend. The method proposed is evaluated and compared with simpler approaches in a simulation study. Finally the method is applied to a subset of the data from the 'Concerted action on seroconversion to AIDS and death in Europe' study.