零截断复发事件数据的分层回归分析
Stratified Regression Analysis of Zero-Truncated Recurrent Event Data
AI总结：
本文针对零截断复发事件数据，提出一种结合人口普查补充信息的分层Cox回归模型及估计方法，并证明其一致性和渐近正态性，模拟显示其优于仅用零截断数据的最大似然估计。
摘要：
本文的动机来源于一项正在进行的儿童心理健康护理（PMHC）项目，在该项目中，心理健康相关急诊科（MHED）就诊记录提取自基于人群的行政数据库。本文的一个特别关注点是理解在一般人群中，就诊发生如何依赖于过去的发生情况。在特定受试者的时间窗口内，只能获得经历MHED就诊的受试者的信息。因此，MHED就诊可被视为零截断复发事件。一些人口普查信息可作为研究期间未发生MHED就诊的受试者协变量的补充信息加以利用。我们考虑了一种创新的分层Cox回归模型，该模型基于强度，但仅需要事件历史的概要信息。我们提出了一种将零截断数据与一些补充信息相结合的估计方法。我们建立了所提估计量的一致性和渐近正态性。通过模拟评估了该估计量的有限样本性质，结果表明所提估计量优于仅基于零截断数据的最大似然估计量。我们通篇使用PMHC项目来说明所提出的方法。
英文摘要：
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.
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