New Statistical Models for Recurrent Event Data

重复事件数据的新统计模型

• 批准号: 6787187
• 负责人: EDSEL Aldea PENA
• 金额: $ 29.59万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6787187
• 关键词: clinical research; computer data analysis; computer human interaction; computer program /software; computer system design /evaluation; data collection methodology /evaluation; human data; longitudinal human study; mathematical model; model design /development; relapse /recurrence; statistics /biometry

DESCRIPTION (provided by applicant): Recurrent events are prevalent in many longitudinal studies in biomedical and public health settings. A major goal when dealing with recurrent events is to relate the event inter-occurrence times to relevant concomitant variables. Such knowledge provides guidance in formulating public health policies, advocating proper social behavior, determining appropriate medical actions, or dictating courses of action. Aside from the effects of concomitant variables on the event inter-occurrence times, three other aspects need to be considered. First, when the event occurs, an intervention is usually instituted to prolong the reoccurrence of the event. Second, the weakening effect of accumulating event occurrences on the subject needs to be accounted for. Third, since there may be more than one event for a subject, the inter-occurrence times may be affected by unobserved factors, which induce association among these times. A general class of stochastic models which simultaneously incorporate these aspects, and which subsumes many existing specialized intensity models, has been proposed in Pena and Hollander (2002). For this new and general class of models, this project aims (a) to develop and examine appropriate statistical methods for estimating relevant model parameters; (b) to examine the impact of the data accrual aspects of the recurrent event model; (c) to develop methods for validating the model, performing goodness-of-fit tests, and diagnosing the model; and (d) to apply the resulting new statistical methods to real biomedical and public health data sets. The results of this project will provide biomedical and public health practitioners a wide class of stochastic models and new statistical methods appropriate for analyzing recurrent event data arising in public health and biomedical studies such as for instance tumor occurrence, cancer studies, mental health, and many others.

描述（由申请人提供）：复发事件在生物医学和公共卫生环境中的许多纵向研究中普遍存在。处理复发事件的主要目标是将事件发生间隔时间与相关伴随变量联系起来。这些知识为制定公共卫生政策、倡导适当的社会行为、确定适当的医疗行动或规定行动方针提供了指导。除了伴随变量对事件发生间隔时间的影响外，还需要考虑其他三个方面。首先，当事件发生时，通常会采取干预措施，以延长事件的再次发生。其次，需要考虑事件发生的累积对主体的弱化作用。第三，由于受试者可能有不止一个事件，因此发生间隔时间可能受到未观察到的因素的影响，这些因素会诱导这些时间之间的关联。佩纳和霍兰德（2002年）提出了一类随机模型，它们同时包含了这些方面，并包含了许多现有的专门强度模型。对于这类新的一般模型，本项目的目标是：（a）制定和审查用于估计相关模型参数的适当统计方法;（B）审查经常性事件模型的数据累积方面的影响;（c）制定验证模型、进行拟合优度检验和诊断模型的方法;以及（d）将由此产生的新统计方法应用于真实的生物医学和公共卫生数据集。该项目的结果将为生物医学和公共卫生从业者提供广泛的随机模型和新的统计方法，适用于分析公共卫生和生物医学研究中出现的经常性事件数据，例如肿瘤发生，癌症研究，心理健康等。