A Bayesian Approach in Differential Equation Dynamic Models Incorporating Clinical Factors and Covariates.
A Bayesian Approach in Differential Equation Dynamic Models Incorporating Clinical Factors and Covariates.
复制标题
结合临床因素和协变量的微分方程动态模型中的贝叶斯方法。
DOI：
10.1080/02664760802578320
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发表时间：
2010
影响因子：
1.5
通讯作者：
Huang,Yangxin
中科院分区：
文献类型：
--
作者：
Huang,Yangxin
一种病毒学标志物，HIV RNA拷贝数或病毒载量，目前用于评估艾滋病临床试验中的抗逆转录病毒（ARV）疗法。该标志物可用于评估治疗的抗病毒效力，但在长期治疗评估过程中可能容易受到药物暴露和耐药性等临床因素以及基线特征的影响。HIV动态研究对理解HIV发病机制和抗逆转录病毒治疗策略有重要贡献。病毒动力学模型可以通过微分方程来制定，但只有有限的统计方法来估计这种模型或评估其与观测数据的一致性。本文发展了基于机制的非线性微分方程模型，用于表征ARV治疗的长期病毒动力学。在该模型中，我们不仅将临床因素（药物暴露和易感性），而且将基线协变量（基线病毒载量，CD4计数，体重或年龄）纳入治疗疗效函数。贝叶斯非线性混合效应建模方法的研究与应用艾滋病临床试验研究。临床因素与基于协变量的模型的混杂相互作用的影响进行了比较，使用偏差信息标准（DIC），贝叶斯版本的经典偏差模型评估，设计从复杂的分层模型设置。探讨基线协变量与临床混杂因素和药物疗效之间的关系。此外，我们通过DIC比较了包含四个基线协变量的模型，并提出了一些有趣的发现。我们的研究结果表明，考虑到随时间变化的临床因素以及基线特征，对HIV动力学和病毒学反应进行建模可能在理解HIV发病机制，设计新的治疗策略以长期护理艾滋病患者方面发挥重要作用。
A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This marker can be used to assess the antiviral potency of therapies, but may be easily affected by clinical factors such as drug exposures and drug resistance as well as baseline characteristics during the long-term treatment evaluation process. HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and ARV treatment strategies. Viral dynamic models can be formulated through differential equations, but there has been only limited development of statistical methodologies for estimating such models or assessing their agreement with observed data. This paper develops mechanism-based nonlinear differential equation models for characterizing long-term viral dynamics with ARV therapy. In this model we not only incorporate clinical factors (drug exposures, and susceptibility), but also baseline covariate (baseline viral load, CD4 count, weight, or age) into a function of treatment efficacy. A Bayesian nonlinear mixed-effects modeling approach is investigated with application to an AIDS clinical trial study. The effects of confounding interaction of clinical factors with covariate-based models are compared using the deviance information criteria (DIC), a Bayesian version of the classical deviance for model assessment, designed from complex hierarchical model settings. Relationships between baseline covariate combined with confounding clinical factors and drug efficacy are explored. In addition, we compared models incorporating each of four baseline covariates through DIC and some interesting findings are presented. Our results suggest that modeling HIV dynamics and virologic responses with consideration of time-varying clinical factors as well as baseline characteristics may play an important role in understanding HIV pathogenesis, designing new treatment strategies for long-term care of AIDS patients.