Unified Dynamic Modeling of Event Time Data with Semiparametric Profile Estimating Functions: Theory, Computing, and Applications

使用半参数轮廓估计函数对事件时间数据进行统一动态建模：理论、计算和应用

• 批准号: 0805965
• 负责人: Jun Yan
• 金额: $ 15万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0805965&HistoricalAwards=false
• 关键词: Unified; Dynamic; Modeling; Event; Time

Event time data under various censoring schemes arise in a variety of fields. The temporal dynamics of covariate effects on the occurrence of events can be flexibly studied in a partly functional regression model on the mean function of temporal processes defined by event times, with some covariate coefficients time-varying while others time-independent. Statistical inferences are challenging with the presence of multiple time-varying components, particularly when event times are not continuously but discretely observed, as under interval censoring. The investigator studies a unified semiparametric profile estimating function approach and an estimated version of it when the event times are only discretely observed. For continuously observed data, the model parameters are estimated from an algorithm that alternately updates the current estimate between an estimating equation for time-varying coefficients and an estimating equation for time-independent coefficients. For interval censored or doubly censored data, estimating functions will be estimated first using multiple imputation and then solved alternately. The methodology will be implemented in an R package under the quality assurance scheme of the R Project. The proposed approach provides a unified theoretical framework of dynamic regression models for event times, enabling a synthesized investigation of computingalgorithms and statistical inferences. It allows efficient estimation and successive hypotheses test of covariate effects. Application of the method to a cystic fibrosis disease registry data will generate new knowledge on the temporal nature of the association between malnutrition and pulmonary disease progression.The proposed method is motivated by the need of modeling temporal dynamics of covariate effects on the occurrence of certain events. Events of interests can be, for example, the recurrences of some chronic disease symptoms such as lung infection in cystic fibrosis patients, or the breakdowns of some engineering or electronic systems such as access failure of computer disks. A blend of time-varying effects and time-independent effects allows most flexibility in assessing the efficacy of a treatment of a disease or a design of new system over time. The method has an impact on the practice of event time data analysis when the temporal nature of covariate effects are of important interests. A publicly available software will get the methodology into the hands of those who will profit from using them, and, therefore, help to understand the temporal dynamics of covariate effects on event occurrences.

各种审查方案下的事件时间数据在各个领域中出现。可以在事件时间定义的时间过程的平均功能中灵活研究协变量对事件发生的时间动力学，并具有一些协方差系数时间变化，而其他协方差。统计推断在存在多个时间变化的组件的情况下，尤其是在事件时间不连续而不是离散地观察到的情况下，就像间隔审查一样。研究人员研究了仅在事件时间被离散观察到统一的半参数估计函数方法和IT估计版本的统一版本。对于连续观察到的数据，模型参数是根据算法估算的，该算法交替更新时间变化系数的估计方程与时间无关系数的估计方程之间的当前估计值。对于间隔审查或双重审查数据，将首先使用多个插补估算估计功能，然后交替求解。该方法将根据R项目的质量保证计划在R软件包中实施。所提出的方法为事件时间提供了动态回归模型的统一理论框架，从而实现了对计算值和统计推断的合成研究。它允许有效的估计和连续的假设检验协变量效应。该方法在囊性纤维化疾病登记处的应用将产生有关营养不良和肺部疾病进展之间关联的时间性质的新知识。该方法是出于需要建模对某些事件发生的协方差影响的时间动力学的需要。感兴趣的事件可以例如，例如某些慢性疾病症状的复发，例如囊性纤维化患者中的肺部感染，或某些工程或电子系统的崩溃，例如计算机磁盘的访问失败。随时间变化的效果和时间无关的效果的混合物，可以最大程度地灵活地评估疾病治疗的功效或随着时间的推移设计新系统的功效。当协变量效应具有重要利益时，该方法会影响事件时间数据分析的实践。公开可用的软件将使方法能够从使用它们中获利的人的手中，因此有助于了解协变量对事件发生的效果的时间动态。