使用面板二元数据的贝叶斯比例均值模型——在健康与退休研究中的应用
A Bayesian Proportional Mean Model Using Panel Binary Data-An Application to Health and Retirement Study
AI总结：
针对面板二元复发事件数据缺乏贝叶斯方法的问题，提出贝叶斯比例均值模型及验证、选择与影响诊断技术，并通过模拟和 HRS 数据分析验证其有效性。
摘要：
在复发事件研究中，当受试者在离散时间点接受观察，且仅记录每个观察窗口内的复发事件状态时，就会产生面板二元数据。由于随访期间存在回忆困难或参与者隐私顾虑，此类数据在纵向研究中频繁出现，因此需要严谨的统计分析。尽管已有处理此类数据的频率学派方法，但贝叶斯方法在很大程度上仍未得到探索。本文提出了一种高效的贝叶斯比例均值模型，用于利用面板二元数据分析复发事件。除估计程序外，本文还介绍了用于模型验证、选择和贝叶斯影响诊断的技术。模拟研究证明了该方法在不同实际场景中的有效性和稳健性。随后，所提方法被应用于分析最新版本的 Health and Retirement Study 数据集，识别影响老年人就医次数的关键风险因素。因此，该分析能够为老龄化人群的医疗保健利用模式提供有价值的见解。
英文摘要：
In recurrent event studies, panel binary data arise when subjects are observed at discrete time points and only the recurrent event status within each observation window is recorded. Such data frequently occur in longitudinal studies due to recall difficulties or participants' privacy concerns during follow-ups, necessitating rigorous statistical analysis. While frequentist methods exist for handling such data, Bayesian approaches remain largely unexplored. This article proposes an efficient Bayesian proportional mean model for analysing recurrent events using panel binary data. In addition to the estimation procedure, the article introduces techniques for model validation, selection, and Bayesian influence diagnostics. Simulation studies demonstrate the method's effectiveness and robustness in different practical scenarios. The proposed approach is then applied to analyse the latest version of the Health and Retirement Study dataset, identifying key risk factors influencing doctor visits among the elderly. The analysis is therefore capable of providing valuable insights into healthcare utilisation patterns in ageing populations.
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。
