A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies.
A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies.
复制标题
混合和截断纵向数据和生存数据的联合模型，应用于艾滋病毒疫苗研究。
DOI：
10.1093/biostatistics/kxx047
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Gilbert,PeterB
中科院分区：
文献类型：
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作者：
Yu,Tingting;Wu,Lang;Gilbert,PeterB
在艾滋病毒疫苗研究中，一个主要的研究目标是确定可能与艾滋病毒感染风险相关的纵向测量的免疫反应生物标志物。这一目标可以通过纵向和生存数据的联合建模来评估。艾滋病毒疫苗数据联合模型因以下问题而变得复杂：(i)由于量化下限较低，一些纵向数据出现左截断；（ii）纵向变量的混合类型；（三）纵向测量的测量误差和缺失值；（iv）与似然推断相关的计算挑战。在本文中，我们提出了一个复杂的纵向和生存数据的联合模型和一种计算效率高的近似似然推断方法来同时解决上述问题。特别是，我们的模型没有对截断值做出不可验证的分布假设，这与文献中常用的方法不同。参数估计基于h-似然方法，该方法计算效率高，提供近似似然推断。此外，我们提出了一种利用自适应高斯-埃尔米特方法估计基于h-似然的参数估计的标准误差的新方法。仿真研究表明，我们的方法性能良好，计算效率高。并对数据进行了综合分析。
In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inference. In this article, we propose a joint model of complex longitudinal and survival data and a computationally efficient method for approximate likelihood inference to address the foregoing issues simultaneously. In particular, our model does not make unverifiable distributional assumptions for truncated values, which is different from methods commonly used in the literature. The parameters are estimated based on the h-likelihood method, which is computationally efficient and offers approximate likelihood inference. Moreover, we propose a new approach to estimate the standard errors of the h-likelihood based parameter estimates by using an adaptive Gauss–Hermite method. Simulation studies show that our methods perform well and are computationally efficient. A comprehensive data analysis is also presented.