Methodological Issues in Event Histories Analysis

事件历史研究是通过监测受试者或个人一段时间的情况，并根据一个或多个动态过程收集关于特定事件或条件发生的信息来进行的。它的特点是多个事件和纵向观测。在过去的二十年中，对于多变量生存时间、复发事件和多状态模型以及复杂结果的更一般的事件历史过程的方法学的发展引起了相当大的关注。*典型的流行队列研究只包括在招募时处于特定状态(例如，患有疾病)的个体，然后是最典型的计划的随访时间。如果疾病存活时间是主要关注的，那些在招募之前患病但死亡的人不符合条件会导致估计中的选择偏差。在“单一事件”生存分析的背景下，这被归结为一个有长度偏见的问题，它在文献中已经得到了很好的研究。然而，在多状态事件历史分析的一般框架中，对响应相关抽样的研究还很少。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理。研究处理间歇性评估的方法。*事件史研究中经常出现重复或重复的事件。模型包括基于速率函数和事件计数的泊松过程；更新过程关注间隔或等待时间和风险函数。当涉及具有反复发作的多个事件过程时，就会出现复杂的关联结构。本研究旨在开发一种利用不同随机效应的联合方法来表征间隔时间之间的关联，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以及不同类型事件持续时间之间的过程间关联。创新的方法将被用来解决构建高维多变量分布的困难。还将考虑基于复合似然的联合方法。*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，在感兴趣的结果是到任何组成部分终点首次出现的时间时，评估对复合终点的治疗效果往往是有吸引力的。当间歇性地评估一个组成部分(例如进展)时，在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调查右端点归罪的影响，这是一种在实践中处理区间审查的标准方法。调整样本量的方法也令人感兴趣。*

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. *** **