消融术后房颤的概率：使用参数化非线性时间分解混合效应模型。
Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.
作者信息
Rajeswaran Jeevanantham, Blackstone Eugene H, Ehrlinger John, Li Liang, Ishwaran Hemant, Parides Michael K
机构信息
1 Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH, USA.
2 The University of Texas MD Anderson Cancer Center, University of Texas, Houston, TX, USA.
出版信息
Stat Methods Med Res. 2018 Jan;27(1):126-141. doi: 10.1177/0962280215623583. Epub 2016 Jan 5.
Abstract
Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.
摘要
心房颤动是一种心律失常疾病，其中心脏的电信号变得不规则。心房颤动（二元反应）的概率通常以一种结构化的方式随时间变化，相关风险因素的影响也是如此。本文提出了一种广义非线性混合效应模型，使用时间分解方法来估计心房颤动的时间相关概率，以揭示心房颤动概率的模式及其决定因素。该方法推广到对纵向二元数据进行患者特异性分析，协变量可能具有随时间变化的效应，并且不同的患者特异性随机效应影响不同的时间阶段。通过心胸外科临床试验网络开展的一项由美国国立卫生研究院资助的临床试验，利用每周通过电话远程监测获得的纵向测量心房颤动数据，说明了该模型的动机和应用。
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