Study design and statistical methods to account for unexplained heterogeneity in medical studies with a time to event outcome

在许多医学研究中，对某些特定事件的时间的评估是主要关注的，例如疾病进展的时间。回归模型通常适用于这类数据，其假设是，个体经历事件的风险的异质性可以通过已知的协变量来解释。然而，共享相同协变量值的个体通常在其事件风险方面仍然表现出异质性，异质性的来源未知，至少在研究开始时是这样。未观察到的或未知的属性可能会影响结局，研究人群的一部分（未知）可能对事件不敏感，或研究定义为具有影响结局的未知属性的观察单位群。也可能存在不止一个异质性来源。研究人群的异质性对临床研究具有重要意义：首先，它降低了医学研究的效率，因此需要更大的样本量。其次，它降低了研究结果的精确度，从而限制了它们用于未来患者的决策，例如关于治疗决策或随访访视的适当安排。通过应用研究项目，我们的目标是获得统计设计和方法，减少和/或适当模拟医学研究中的患者异质性，并具有事件发生时间结果，从而提高医学研究的（成本）效率，以及从研究结果中得出的临床决策的准确性。特别是，我们的目标是调查统计估计，当异质性来源被错误指定时，已建立的事件时间模型的统计推断和预测性能，推导出方法，以动态预测一个人的结果，在一个潜在的时间依赖的机会是不敏感的事件（治愈）的存在并研究灵活的设计，将研究重点放在更同质的亚群上。所有方法都将在模拟和真实的研究数据上进行评价和说明。

In many medical studies, the evaluation of the time to some particular event is of primary interest, for example the time to disease progression. Regression models are commonly fitted to these kinds of data relying on the assumption that heterogeneity in the individuals' risk to experience events can be explained by known covariates. Nevertheless, individuals sharing the same covariate values often appear to be still heterogeneous in their event risk with the sources of heterogeneity being unknown, at least at the beginning of the study. Unobserved or unknown attributes might affect the outcome, an (unknown) part of the study population might be nonsusceptible for the event, or studies are defined on clusters of observational units that share unknown attributes affecting the outcome. Also more than one source of heterogeneity might be present. A heterogeneous study population has important implications for clinical research: First, it decreases the efficiency of medical studies and thus calls for larger sample size. Secondly, it decreases the precision of study results and thus limits their use for decision making for future patients, for example with respect to therapeutic decisions or the proper scheduling of follow-up visits. With the applied research project we aim to derive statistical designs and methods that decrease and/or properly model the patients heterogeneity in medical studies with a time to event outcome and thus will improve the (cost-)efficiency of medical studies, and the accuracy of clinical decisions that are derived from study results.Particularly, we aim to investigate the statistical estimates, statistical inference and the prediction performance of established time to event models when the source of heterogeneity is misspecified, derive methods to dynamically predict an individuals outcome in the presence of a potentially time-dependent chance of being nonsusceptible for the event (cured) and investigate flexible designs to focus a study on a more homogeneous subpopulation. All methods will be evaluated and illustrated on simulated and real study data.