标题： 零截尾复发事件数据的分层回归分析 显示英文标题

标题： Stratified Regression Analysis of Zero-Truncated Recurrent Event Data

摘要： 本文的研究动机源于一项正在进行的儿科心理健康护理（PMHC）项目，在该项目中，从基于人群的行政数据库中提取了与心理健康相关的急诊科（MHED）就诊记录。本文的一个特别兴趣在于理解在一般人群中，就诊的发生如何依赖于过去的就诊发生情况。仅在特定个体的时间窗口内可以获得经历MHED就诊对象的信息。因此，MHED就诊可被视为零截尾重复事件。可以利用一些人口普查信息作为研究期间没有MHED就诊对象的协变量的补充信息。我们考虑了一个创新的分层Cox回归模型，这是一种基于强度的模型，但只需要事件历史的摘要。我们提出了一种将零截尾数据与一些补充信息相结合的估计程序。我们建立了所提出估计量的一致性和渐近正态性。通过模拟评估了估计量的有限样本性质，结果显示所提出的估计器比仅基于零截尾数据的最大似然估计器性能有所提高。我们使用PMHC项目来说明本文提出的这种方法。 显示英文摘要

摘要： This paper is motivated by an ongoing pediatric mental health care (PMHC) program in which records of mental health-related emergency department (MHED) visits are extracted from population-based administrative databases. A particular interest of this paper is to understand how the visit occurrence depends on the occurrences in the past in a general population. Only information on subjects experiencing MHED visits is available within a subject-specific time window. Thus, the MHED visits may be viewed as zero-truncated recurrent events. Some population census information can be utilized as supplementary information on the covariates of subjects without MHED visits during the study period. We consider an innovative stratified Cox regression model, which is an intensity-based model but requiring only a summary of the event history. We propose an estimation procedure with zero-truncated data integrated with some supplementary information. We establish the consistency and asymptotic normality of the proposed estimator. The finite-sample properties of the estimator are evaluated by simulation, which demonstrates improved performance of the proposed estimator over the maximum likelihood estimator based on zero-truncated data only. We use the PMHC program to illustrate the proposed approach throughout the paper.