Characterization of HIV viral rebound after the discontinuation of antiretroviral therapy is central to HIV cure research. We propose a parametric nonlinear mixed effects model for the viral rebound trajectory, which often has a rapid rise to a peak value followed by a decrease to a viral load set point. We choose a flexible functional form that captures the shapes of viral rebound trajectories and can also provide biological insights regarding the rebound process. Each parameter can incorporate a random effect to allow for variation in parameters across individuals. Key features of viral rebound trajectories such as viral set points are represented by the parameters in the model, which facilitates assessment of intervention effects and identification of important pretreatment interruption predictors for these features. We employ a stochastic expectation‐maximization (StEM) algorithm to incorporate HIV‐1 RNA values that are below the lower limit of assay quantification. We evaluate the performance of our model in simulation studies and apply the proposed model to longitudinal HIV‐1 viral load data from five AIDS Clinical Trials Group treatment interruption studies.

中文翻译：

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治疗中断后 HIV 病毒载量反弹的灵活非线性混合效应模型。


停止抗逆转录病毒治疗后艾滋病毒病毒反弹的特征是艾滋病毒治愈研究的核心。我们提出了病毒反弹轨迹的参数非线性混合效应模型，该模型通常会快速上升到峰值，然后下降到病毒载量设定点。我们选择了一种灵活的功能形式，可以捕获病毒反弹轨迹的形状，还可以提供有关反弹过程的生物学见解。每个参数都可以包含随机效应，以允许个体之间的参数变化。病毒反弹轨迹的关键特征（例如病毒设定点）由模型中的参数表示，这有助于评估干预效果并识别这些特征的重要预处理中断预测因子。我们采用随机期望最大化 (StEM) 算法来合并低于测定定量下限的 HIV-1 RNA 值。我们评估了我们的模型在模拟研究中的性能，并将所提出的模型应用于来自五项艾滋病临床试验组治疗中断研究的纵向 HIV-1 病毒载量数据。