Electronic monitoring device event modelling on an individual-subject basis using adaptive Poisson regression
Electronic monitoring device event modelling on an individual-subject basis using adaptive Poisson regression
复制标题
DOI：
10.1002/sim.1624
复制
发表时间：
2004-03-15
影响因子：
2
通讯作者：
Williams, AB
中科院分区：
文献类型：
--
作者：
Knafl, GJ;Fennie, KP;Williams, AB
一种自适应方法泊松回归模型提出了分析事件数据从电子设备监测服药。重点是将这种方法应用于单个主题的数据，尽管它也适用于多个主题的数据。这种方法提供了粘附模式的可视化以及实际设备使用与处方药物服用的客观比较。使用电子药瓶瓶盖开口的数据进行示例分析，以监测接受高活性抗逆转录病毒治疗的艾滋病毒感染者的依从性。建模方法包括对观察期进行分区，计算该分区中间隔的分组事件计数/率，并根据进入研究后使用泊松回归的经过时间对这些事件计数/率进行建模。这些模型是基于自适应选择的经过时间的幂变换集，由基于规则的启发式搜索通过任意参数模型集确定，从而有效地生成光滑的非参数回归拟合数据。模型使用k倍似然交叉验证进行比较。版权所有：John Wiley Sons， Ltd. 2004
An adaptive approach to Poisson regression modelling is presented for analysing event data from electronic devices monitoring medication-taking. The emphasis is on applying this approach to data for individual subjects although it also applies to data for multiple subjects. This approach provides for visualization of adherence patterns as well as for objective comparison of actual device use with prescribed medication-taking. Example analyses are presented using data on openings of electronic pill bottle caps monitoring adherence of subjects with HIV undergoing highly active antiretroviral therapies. The modelling approach consists of partitioning the observation period, computing grouped event counts/rates for intervals in this partition, and modelling these event counts/rates in terms of elapsed time after entry into the study using Poisson regression. These models are based on adaptively selected sets of power transforms of elapsed time determined by rule-based heuristic search through arbitrary sets of parametric models, thereby effectively generating a smooth non-parametric regression fit to the data. Models are compared using k-fold likelihood cross-validation. Copyright (C) 2004 John Wiley Sons, Ltd.