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最重要的，例如疾病进展的时间。回归模型通常适用于这些类型的数据，依赖于这样的假设，即个体经历事件的风险的异质性可以用已知的协变量来解释。然而，拥有相同协变量值的个体在其事件风险中往往表现出异质性，异质性的来源是未知的，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果，研究人群中的(未知)部分可能不受事件的影响，或者研究定义在共享影响结果的未知属性的观察单位群上。此外，可能存在不止一个异质性来源。异质研究群体对临床研究有重要影响：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，从而限制了它们在未来患者决策方面的使用，例如关于治疗决策或适当安排后续访问的决策。在应用研究项目中，我们的目标是得出统计设计和方法，以减少和/或适当地模拟患者在医学研究中的异质性和到事件的结果，从而提高医学研究的(成本)效率，以及从研究结果得出的临床决策的准确性。具体地说，我们的目标是调查已建立的到事件的时间模型的统计估计、统计推断和预测性能，当异质性的来源被错误指定时，推导出在存在潜在的与时间相关的对事件不敏感的机会(治愈)的情况下动态预测个体结果的方法，并调查灵活的设计以将研究集中在更同质的亚群上。所有方法都将在模拟和真实研究数据上进行评估和说明。

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.