使用多种纵向生物标志物定量HIV感染的近期性
Quantifying the recency of HIV infection using multiple longitudinal biomarkers
摘要：
了解艾滋病毒感染者血清转化的时间，即免疫系统开始对艾滋病毒感染作出反应的时间，在设计和实施减少艾滋病毒流行影响的干预措施方面发挥着至关重要的作用。已经开发了许多生物标志物来区分近期和长期HIV感染，基于对HIV的抗体反应。为了在个体水平上量化感染的新近度，我们建议使用贝叶斯混合效应模型，从具有已知血清转换时间的一组个体的观察中表征这种生物标志物的生长。我们将这种生长模式的知识与新诊断个体的观察结果相结合，以估计诊断前X个月内发生血清转化的概率。我们通过模拟研究探索影响我们估计近因能力（例如生长率）的不同生物标志物的特征。特别是，我们发现，通过使用两个生物标志物的联合模型，考虑它们的相关性，而不是单一生物标志物的单变量模型，预测能力得到提高。
英文摘要：
Knowledge of the time at which an HIV-infected individual seroconverts, when the immune system starts responding to HIV infection, plays a vital role in the design and implementation of interventions to reduce the impact of the HIV epidemic. A number of biomarkers have been developed to distinguish between recent and long-term HIV infection, based on the antibody response to HIV. To quantify the recency of infection at an individual level, we propose characterising the growth of such biomarkers from observations from a panel of individuals with known seroconversion time, using Bayesian mixed effect models. We combine this knowledge of the growth patterns with observations from a newly diagnosed individual, to estimate the probability seroconversion occurred in the X months prior to diagnosis. We explore, through a simulation study, the characteristics of different biomarkers that affect our ability to estimate recency, such as the growth rate. In particular, we find that predictive ability is improved by using joint models of two biomarkers, accounting for their correlation, rather than univariate models of single biomarkers.
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