Statistical Analysis for Mixed Recurrent-Event and Panel-Count Data

描述（由申请人提供）：摘要本应用程序解决了混合循环事件和面板计数数据的新分析策略的开发和应用。虽然循环事件数据和面板计数数据都是由循环事件过程生成的，但它们具有不同的观察系统。前者连续观察被试，后者仅在离散时间点观察被试。因此，复发事件数据记录了复发事件的所有发生时间，而面板计数数据仅记录了观测时间点之间的事件。有可能在单个队列中，一些受试者有重复事件数据，而另一些受试者有小组计数数据，或者每个受试者在某些时期有重复事件数据，在其他时期有小组计数数据。在长期随访研究中，这些混合数据并不罕见。目前常见的做法是对这些复杂的数据进行近似或简化，从而得出可能具有误导性的结论。在事件历史研究中，迫切需要开发直观、高效、计算可行的方法来分析复杂的数据。本项目建议使用著名的纵向儿童癌症幸存者研究（CCSS）的数据：1)为这些混合数据开发平均函数的非参数估计和非参数双样本比较程序；2)建立了基于半参数估计方程的比例平均模型和基于半参数估计方程的加性速率模型的回归分析方法；3)将Aim 2中开发的方法扩展到多元混合循环事件和面板计数数据。这些方法对于复杂事件历史数据的研究具有很强的统计学和临床相关性。

DESCRIPTION (provided by applicant): Abstract This application addresses the development and application of new analytic strategies for mixed recurrent-event and panel-count data. While recurrent-event data and panel-count data are both generated from recurrent-event processes, they have different observation systems. In the former, subjects are observed continuously and in the latter, subjects are observed only at discrete time points. Consequently, recurrent-event data record all occurrence times of recurrent events, while panel-count data record only the events between observation time points. It is possible that in a single cohort, some subjects have recurrent-event data while others have panel-count data, or every subject has recurrent-event data during some periods and has panel-count data during other periods. It is not unusual to have these mixed data in long-term follow-up studies. The current common practice is to approximate or simplify these complex data, resulting in potentially misleading conclusions. There is an urgent need to develop intuitive, efficient, and computationally feasible methods for analyzing complex data in event history studies. This project proposes to use data from the renown longitudinal Childhood Cancer Survivor Study (CCSS) to: 1) develop both a nonparametric estimation of the mean function and a procedure of nonparametric two-sample comparison for these mixed data; 2) Develop a semiparametric estimating equation-based method for a proportional mean model and a semiparametric estimating equation-based method for an additive rate model for regression analysis; and, 3) Extend the methods developed in Aim 2 for multivariate mixed recurrent-event and panel-count data. These approaches potentially have strong statistical and clinical relevance for the study of complex event history data.