通过带有时变系数的分层回归分析零截断复发事件
Analyzing Zero-Truncated Recurrent Events by Stratified Regression with Time-Varying Coefficients
AI总结：
针对零截断复发事件数据，提出结合人口普查信息的时变系数分层Cox回归参数估计方法，经模拟验证有限样本性能并确立渐近性质，用PMHC项目数据加以说明。
摘要：
本文提出了一种分析零截断复发事件数据的策略。受儿科精神卫生保健（PMHC）项目的启发，我们重点关注事件发生如何依赖于过去的事件发生情况。我们考虑了一种带有时变系数的分层Cox回归模型，并提出了一种结合零截断数据与人口普查信息来估计模型参数的流程。我们通过模拟评估了所提出估计量的有限样本性能，并建立了其渐近性质。全文使用PMHC项目的数据来引出并说明所提出的方法。
英文摘要：
This paper presents a strategy for analyzing zero-truncated recurrent events data. Motivated by a pediatric mental health care (PMHC) program, we are particularly concerned with how the event occurrence depends on the occurrences in the past. We consider a stratified Cox regression model with time-varying coefficients and propose a procedure for estimating the model parameters using the zero-truncated data integrated with population census information. We evaluate the finite-sample performance of the proposed estimator through simulation and establish its asymptotic properties. Data from the PMHC program are used throughout the paper to motivate and to illustrate the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
