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

各种删失方案下的事件时间数据出现在各种领域。协变量对事件发生的影响的时间动态可以灵活地在由事件时间定义的时间过程的平均函数的部分函数回归模型中进行研究，其中一些协变量系数随时间变化，而另一些则与时间无关。统计推断是具有挑战性的存在下，多个随时间变化的组件，特别是当事件时间不是连续的，但离散观察，如在区间删失。研究了事件时间离散观测时统一的半参数轮廓估计函数方法及其估计形式。对于连续观测的数据，模型参数估计从一个算法，交替更新的时变系数的估计方程和时间无关系数的估计方程之间的当前估计。对于区间删失或双删失数据，将首先使用多重插补估计估计函数，然后交替求解。该方法将在R项目质量保证计划下的R包中实施。该方法为事件时间动态回归模型提供了统一的理论框架，使计算算法和统计推断的综合研究成为可能。它允许有效的估计和协变量效应的连续假设检验。将该方法应用于囊性纤维化疾病登记数据将产生新的知识营养不良和肺部疾病progression.The所提出的方法之间的关联的时间性质的协变量的影响，某些事件的发生建模的时间动力学的需要的动机。感兴趣的事件可以是，例如，一些慢性疾病症状的复发，如囊性纤维化患者的肺部感染，或一些工程或电子系统的故障，如计算机磁盘的访问故障。随时间变化的效应和与时间无关的效应的混合使得随着时间的推移评估疾病治疗或新系统设计的功效具有最大的灵活性。当协变量效应的时间性质具有重要意义时，该方法对事件时间数据分析的实践产生了影响。一个公开可用的软件将得到的方法到那些谁将从中获利，并因此，有助于了解事件发生的协变量效应的时间动态。

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.