一个包含服药依从性、耐药性和协变量的HIV应答的动态贝叶斯非线性混合效应模型
A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates
摘要：
HIV动态研究对了解HIV的发病机制和艾滋病患者的抗病毒治疗策略有重要意义。建立长期抗逆转录病毒（ARV）治疗期间病毒学应答与临床因素和协变量的关系对于开发有效的治疗方法非常重要。药物依从性是抗逆转录病毒治疗有效性的重要预测因子，但基于药物事件监测系统（MEMS）数据的依从率的适当决定因素对于预测病毒学结局至关重要。本文的主要目的是调查MEMS依从率的一些总结决定因素对HIV感染患者随时间重复测量的病毒学应答的影响。我们开发了一个基于机制的微分方程模型，考虑药物依从性，病毒对药物的敏感性和基线特征相互作用，以表征治疗开始后的长期病毒学应答。该模型将病毒载量、MEMS粘附性、耐药性和基线协变量完全整合到数据分析中。在这项研究中，我们采用了所提出的模型和相关的贝叶斯非线性混合效应建模方法来评估如何有效地使用MEMS粘附数据预测病毒学应答，并评估MEMS粘附率的每个汇总指标的预测能力。
英文摘要：
HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.
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