论文标题
通过分段的机械非线性模型从左审核的纵向数据中推断随机变化点，并在HIV监视研究中应用
Inferring random change point from left-censored longitudinal data by segmented mechanistic nonlinear models, with application in HIV surveillance study
论文作者
论文摘要
控制艾滋病毒流行病的公共卫生努力的主要目标是在血清转化后尽快诊断和治疗艾滋病毒感染的人。因此，艾滋病毒诊断后抗逆转录病毒疗法（ART）治疗的时间是一个关键的人群水平指标，可用于衡量地方和国家一级的公共卫生计划和政策的有效性。但是，基于人群的艺术启动数据不可用，因为通常通过公共卫生部门间接地测量艺术启动和处方（例如，病毒抑制为代理）。在本文中，我们提出了一个随机变更点模型，以推断出使用HIV监视系统常规报道的个人级HIV病毒负荷的艺术创始时间。为了处理病毒载荷数据的左审查和非线性轨迹，我们制定了柔性分段的非线性混合效应模型，并提出了EM（STEM）算法的随机版本，并与Gibbs Sampler一起进行推理。我们将方法应用于HIV监视数据的随机子集，以推断自诊断以来的ART启动时间，并获得对病毒载荷动态的更多见解。还进行了模拟研究以评估所提出方法的特性。
The primary goal of public health efforts to control HIV epidemics is to diagnose and treat people with HIV infection as soon as possible after seroconversion. The timing of initiation of antiretroviral therapy (ART) treatment after HIV diagnosis is, therefore, a critical population-level indicator that can be used to measure the effectiveness of public health programs and policies at local and national levels. However, population-based data on ART initiation are unavailable because ART initiation and prescription are typically measured indirectly by public health departments (e.g., with viral suppression as a proxy). In this paper, we present a random change-point model to infer the time of ART initiation utilizing routinely reported individual-level HIV viral load from an HIV surveillance system. To deal with the left-censoring and the nonlinear trajectory of viral load data, we formulate a flexible segmented nonlinear mixed effects model and propose a Stochastic version of EM (StEM) algorithm, coupled with a Gibbs sampler for the inference. We apply the method to a random subset of HIV surveillance data to infer the timing of ART initiation since diagnosis and to gain additional insights into the viral load dynamics. Simulation studies are also performed to evaluate the properties of the proposed method.
