In HIV vaccine studies, longitudinal immune response biomarker data are often left-censored due to lower limits of quantification of the employed immunological assays. The censoring information is important for predicting HIV infection, the failure event of interest. We propose two approaches to addressing left censoring in longitudinal data: one that makes no distributional assumptions for the censored data—treating left censored values as a “point mass” subgroup—and the other makes a distributional assumption for a subset of the censored data but not for the remaining subset. We develop these two approaches to handling censoring for joint modelling of longitudinal and survival data via a Cox proportional hazards model fit by h-likelihood. We evaluate the new methods via simulation and analyze an HIV vaccine trial data set, finding that longitudinal characteristics of the immune response biomarkers are highly associated with the risk of HIV infection.

中文翻译：

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在纵向和生存数据的联合建模中审查纵向数据的新方法，并将其应用于HIV疫苗研究。

在HIV疫苗研究中，由于所采用的免疫测定的定量下限较低，纵向免疫反应生物标志物数据经常被左删失。审查信息对于预测HIV感染（感兴趣的失败事件）非常重要。我们提出了两种方法来解决纵向数据中的左审查问题：一种不对审查数据进行分布假设-将左审查值作为“点质量”子组进行处理；另一种对审查数据的子集进行分布假设，但不适用于其余子集。我们开发了这两种方法来处理审查，以通过h似然拟合的Cox比例风险模型对纵向数据和生存数据进行联合建模。我们通过仿真评估新方法并分析HIV疫苗试验数据集，