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Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records
Journal of the American Statistical Association ( IF 3.7 ) Pub Date : 2020-08-26 , DOI: 10.1080/01621459.2020.1801447
Yifei Sun 1, 2, 2, 3 , Charles E McCulloch 1, 2, 2, 3 , Kieren A Marr 1, 2, 2, 3 , Chiung-Yu Huang 1, 2, 2, 3
Affiliation  

Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient's health status influences when and what data are recorded, generating sampling bias in the collected data. In this paper, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration.

中文翻译:

使用电子健康记录中信息性临床访视收集的数据对复发事件进行分析

尽管越来越多地用作组装队列的数据资源,但电子健康记录 (EHR) 带来了许多分析挑战。特别是,患者的健康状况会影响记录数据的时间和内容,从而在收集的数据中产生抽样偏差。在本文中,我们考虑使用 EHR 数据进行复发性事件分析。用于事件风险分析的传统回归方法通常需要在整个随访期间观察协变量的值。在 EHR 数据库中,时间相关的协变量在临床访问期间间歇性地测量,并且这些访问的时间在它取决于病程的意义上是有用的。简单的方法,例如最后一次观察结转的方法,可能会导致估计有偏差。另一方面,复杂的联合模型需要对协变量过程进行额外假设,并且无法轻松扩展以处理多个纵向预测变量。通过结合从估计观察时间过程中得出的采样权重,我们为复发事件的半参数比例率模型开发了一种基于反速率加权和核平滑的新估计程序。所提出的方法不需要协变量过程的模型规范,并且可以轻松处理多个与时间相关的协变量。我们的方法应用于肾移植研究以进行说明。我们为复发事件的半参数比例率模型开发了一种基于反率加权和核平滑的新估计程序。所提出的方法不需要协变量过程的模型规范,并且可以轻松处理多个与时间相关的协变量。我们的方法应用于肾移植研究以进行说明。我们为复发事件的半参数比例率模型开发了一种基于反率加权和核平滑的新估计程序。所提出的方法不需要协变量过程的模型规范,并且可以轻松处理多个与时间相关的协变量。我们的方法应用于肾移植研究以进行说明。
更新日期:2020-08-26
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