Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records

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 article, 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. Supplementary materials for this article are available online.尽管电子健康记录（electronic health records, EHRs）日益被用作构建队列研究的数据源，但这类数据也带来了诸多分析挑战。具体而言，患者的健康状况会影响数据的记录时机与内容，进而在采集的数据中引入抽样偏倚。本文聚焦基于EHR数据的复发事件分析（recurrent event analysis）。常规的事件风险分析回归方法通常要求协变量（covariates）的值在整个随访期内均可被观测到。在EHR数据库中，时变协变量（time-dependent covariates）仅在临床就诊期间被间断性测量，而就诊时机本身具有信息性——其原因在于就诊时机取决于患者的疾病进程。诸如末次观测结转法（last-observation-carried-forward）这类简单方法，可能会得到有偏估计结果。而另一方面，复杂联合模型需要对协变量过程施加额外假设，且难以扩展以处理多个纵向预测变量。通过引入由观测时机过程估计得到的抽样权重，本文针对复发事件的半参数比例率模型（semiparametric proportional rate model），提出了一种基于逆率加权（inverse-rate-weighting）与核平滑（kernel-smoothing）的全新估计流程。所提出的方法无需对协变量过程进行模型设定，且可便捷地处理多个时变协变量。本文将所提方法应用于一项肾移植研究以展示其应用效果。本文的补充材料可在线获取。