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

描述(由申请人提供)：摘要本申请针对重复事件和面板计数混合数据的新分析策略的开发和应用。虽然重复事件数据和面板计数数据都是从重复事件过程中产生的，但它们具有不同的观察系统。在前者中，受试者被连续观察，而在后者中，受试者仅在离散的时间点被观察。因此，重复事件数据记录了重复事件的所有发生时间，而面板计数数据仅记录了观察时间点之间的事件。有可能在一个队列中，一些受试者有重复事件数据，而另一些受试者有面板计数数据，或者每个受试者在某些时期有重复事件数据，而在其他时期有面板计数数据。在长期的跟踪研究中，这些混合数据并不少见。目前的普遍做法是近似或简化这些复杂的数据，从而导致潜在的误导性结论。迫切需要开发直观、高效和计算可行的方法来分析事件历史研究中的复杂数据。本项目建议使用著名的儿童癌症生存纵向研究(CCSS)的数据：1)为这些混合数据开发均值函数的非参数估计和非参数两样本比较程序；2)为回归分析开发基于比例均值模型的半参数估计方程方法和基于加性比率模型的半参数估计方程方法；以及3)扩展目标2中开发的用于多变量混合复发事件和面板计数数据的方法。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.