Methodological Issues in Event Histories Analysis

事件史研究通过随时间监测受试者或个人并根据一个或多个动态过程收集关于某些事件或状况发生的信息来进行。它的特点是多事件和纵向观察。在过去的20年里，相当多的注意力已经转向多变量生存时间，复发事件和多态模型的方法学发展，以及复杂结果的更一般的事件历史过程。典型的流行队列研究仅包括在招募时处于某些状态（例如，患有疾病）的个体，然后在最典型的计划随访时间。如果主要关注疾病生存时间，则招募前死亡的患者不合格会导致估计的选择偏倚。这是投到一个长度偏置的问题，在“单事件”生存分析的背景下，它已经在文献中得到了很好的研究。然而，很少有人做了响应依赖抽样的一般框架中的多态事件的历史分析。本研究旨在量化转换强度估计中的选择偏差，并开发合适的调整方法。将考虑基于参数、弱参数和非参数方法的推断。将研究处理间歇性评估的方法。**在事件史研究中经常出现重复或复发事件。模型包括泊松过程的基础上率函数和计数的事件;更新过程的重点是间隙或等待时间和危险函数。复杂的关联结构出现时，涉及到多个事件的过程与复发发作。本研究的目的是建立一种联合的方法，使用不同的随机效应来表征间隙时间之间的关联，包括同一类型事件的加重和缓解持续时间之间的过程内关联和不同类型事件的持续时间之间的过程间关联。创新的方法将用于解决构建高维多元分布的困难。还将考虑基于复合似然性的联合方法。** 尽管在使用多状态模型或多变量生存模型联合考虑多个事件方面取得了进展，但在实践中，评价对复合终点的治疗效果往往很有吸引力，其中关注的结局是至首次发生任何组分终点的时间。当间歇性评估一个组分（例如进展）时，分析中会出现并发症。我计划在各种建模框架下的复合终点分析中研究右终点插补的影响，这是一种在实践中处理区间删失的标准方法。样本量调整的方法也很有意义。*** **

Event history studies are conducted by monitoring subjects or individuals over time and collecting information on the occurrence of certain events or conditions according to one or multiple dynamic processes. It is characterized by multiple events and longitudinal observations. In the last twenty years, considerable attention has been drawn towards methodology development for multivariate survival times, recurrent events and multistate models and more general event history processes for complex outcomes.******A typical prevalent cohort study only includes individuals being in certain states (e.g. alive with disease) at recruitment time and then at, most typically, planned follow-up times. If the disease survival time is of primary interest, ineligibility of those diseased but dead prior to recruitment results in selection bias in estimation. This is cast into a length biased problem in the context of ``single event" survival analysis and it has been well studied in the literature. However, little has been done for response dependent sampling in a general framework of multistate event history analysis. This research aims to quantify selection biases in the transition intensities estimation, and develop suitable methods for adjustment. Inference based on parametric, weakly parametric and non-parametric approaches will be considered. Methods to deal with intermittent assessment will be investigated.******Repeated or recurrent events often arise in event history studies. Models include Poisson processes based on rate functions and counts of events; renewal processes focus on the gaps or waiting times and hazard functions. Complex association structure arises when multiple event processes with recurrent episodes are involved. This research aims to develop a joint method using different random effects to character the association among gap times, including within-process association among duration of exacerbation and remission of the same type of event and between-processes association among duration of different types of events. Innovative approaches will be used to tackle the difficulty with constructing high-dimensional multivariate distributions. Joint methods based on composite likelihood will also be considered. ******Despite the advances in joint consideration of multiple events using multistate models or multivariate survival models, it is often appealing in practice to evaluate the treatment effect on a composite endpoint where the outcome of interest is the time to the first occurrence of any component endpoint. Complications arise in the analysis when a component (e.g. progression) is intermittently assessed. I plan to investigate the impact of right endpoint imputation, a standard approach to deal with interval censoring in practice, in the composite endpoint analysis under various modelling frameworks. Methods for sample size adjustment are also of interest. *** **