Statistical Analysis for Mixed Outcome Measures in Recurrent Event Studies

项目概要/摘要： 该应用程序解决了混合面板二进制/面板的新分析策略的开发和应用， 事件史研究中的有序/小组计数/复发事件数据。虽然这四种数据类型都是由循环生成的- 事件过程，它们有不同的端点。复发事件数据记录每个事件的发生时间点，面板- 计数数据记录自上次观察以来的事件数量，面板顺序数据记录事件数量 分类，并且小组二元数据记录自上次观察以来是否发生了任何事件。有时我们必须处理 混合数据作为不同的终点，可以在多次观察中为同一变量收集。在4种数据类型中， 重复事件数据提供最大量的相关信息，其次是小组计数、序数和二进制数据。的 关于混合面板二进制/面板顺序/面板计数/复发事件数据的统计文献很少， 这些数据类型大量存在于癌症和非癌症研究中。著名的纵向儿童癌症幸存者 研究（CCSS）就是一个例子。标准的多元或纵向方法不能反映事件的特殊结构 处理底层面板-二进制或有序数据。迫切需要开发直观、高效、 在事件历史研究中分析复杂数据的计算可行方法。在本提案中，我们计划：1） 建立了一种基于似然的半参数估计方法，用于混合面板二进制和面板计数的回归分析 数据; 2）开发用于混合面板二进制数据的回归分析的基于似然的半参数估计方法， 面板计数数据和复发事件数据，并将该方法应用于CCSS数据; 3）开发基于似然性 混合panel-二元/panel-序数/panel-计数/复发事件回归分析的半参数估计方法 数据这些方法将解决在经常性事件的背景下统计分析中的差距，并可能具有强大的 复杂事件历史数据研究的统计学和临床相关性。

Project Summary/Abstract: This application addresses the development and application of new analytic strategies for mixed panel-binary/panel- ordinal/panel-count/recurrent-event data in event history studies. While these 4 data types are all generated from recurrent- event processes, they have different endpoints. Recurrent-event data record the occurring time points of each event, panel- count data record the number of events since the last observation, panel-ordinal data record the number of events categorically, and panel-binary data record if any event has happened since the last observation. At times we must deal with mixed data as different endpoints may be collected for the same variable in multiple observations. Among the 4 data types, recurrent-event data offer the greatest amount of relevant information, followed by panel-count, ordinal, and binary data. The statistical literature on mixed panel-binary/panel-ordinal/panel-count/recurrent-event data is sparse though examples of these data types exist abundantly in cancer and non-cancer studies. The renowned longitudinal Childhood Cancer Survivor Study (CCSS) is an example. Standard multivariate or longitudinal methods cannot reflect the special structure of the event process underlying the panel-binary or ordinal data. There is an urgent need to develop intuitive, efficient, and computationally feasible methods for analyzing complex data in event history studies. In this proposal, we plan to: 1) develop a likelihood-based semiparametric estimation method for regression analysis of mixed panel-binary and panel-count data; 2) develop a likelihood-based semiparametric estimation method for regression analysis of mixed panel-binary data, panel-count data, and recurrent-event data and apply that method to the CCSS data; 3) develop likelihood-based semiparametric estimation methods for regression analysis of mixed panel-binary/pane-ordinal/panel-count/recurrent-event data. These approaches will address a gap in statistical analysis in the context of recurrent events and potentially have strong statistical and clinical relevance for the study of complex event history data.