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Planning and nonparametric inderence for multistate time-to-event data such as diesease occurrences and disease durations

多状态事件时间数据（例如疾病发生和疾病持续时间）的规划和非参数推理

基本信息

批准号：
189200139
负责人：
Professor Dr. Jan Beyersmann
金额：
--
依托单位：
Institut für Medizinische Biometrie und Statistik (IMBI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2011
资助国家：
德国
起止时间：
2010-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/189200139?language=en
关键词：
Planning nonparametric inderence multistate time

项目摘要

The course of a chronic or acute disease often consists of a temporal sequence of events. Multistate models provide a general framework to analyse such disease trajectories. A common approach in clinical studies is to summarize such events in a composite endpoint; e.g. disease-free survival. A competing risks model allows to investigate disease occurrence and death in a more specific way. In case one is also interested in the duration of disease, a more complex multistate model that also takes death after disease occurrence into account is needed.Clinical studies that deal with complex questions need careful planning. In one of the subprojects we have investigated statistical methodology for planning such studies. As one example we considered the clinical question how to show that an innovative prophylactic treatment regimen reduces the occurrence of severe adverse immune reactions while not compromising relapse-free survival of patients after stem-cell transplantation. For rare events, we also considered the use of sampling designs where all patients with the event of interest, e.g. a hospital-acquired infection, but only a sample of patients without the event of interest, will be included in the study. The challenge here is that disease (infection) status is a time-dependent feature and has to be treated as such.In another subproject we investigated alternative approaches that consider these time-dynamic phenomena but allow for simultaneous statistical inference. Such approaches are often simulation and/or resampling based and thus computationally demanding. On the one hand, we have been able to theoretically justify an approach proposed about 20 years ago and, on the other hand, to apply that approach successfully in the analysis of a study on the safety of drugs during pregnancy.In the following we will investigate central issues of planning and statistical inference for studies that call for complex multistate models. In particular, we will concentrate on the so-called exposure-density sampling as an efficient alternative for studies in which exposure to a risk factor is rare. Resampling-/permutation-based statistical approaches are at the core with regards to statistical inference. The planned cooperation shall ensure that both equally important aspects are tied up as closely as possible.

慢性或急性疾病的病程通常由时间序列事件组成。多状态模型提供了分析此类疾病轨迹的一般框架。临床研究中的一种常见方法是将这些事件总结为一个复合终点；例如，无病生存。竞争风险模型允许以更具体的方式调查疾病的发生和死亡。如果人们还对疾病的持续时间感兴趣，则需要一个更复杂的多状态模型，该模型也需要考虑疾病发生后的死亡。处理复杂问题的临床研究需要仔细规划。在其中一个子项目中，我们调查了规划这类研究的统计方法。作为一个例子，我们考虑了一个临床问题，即如何证明一种创新的预防性治疗方案在不影响干细胞移植后患者的无复发生存的同时，减少了严重不良免疫反应的发生。对于罕见事件，我们还考虑使用抽样设计，将所有有感兴趣的事件的患者，例如医院获得性感染，但只包括没有感兴趣的事件的患者的样本纳入研究。这里的挑战是，疾病(感染)状态是一个依赖于时间的特征，必须像这样对待。在另一个子项目中，我们调查了考虑这些时间动态现象但允许同时统计推断的替代方法。这种方法通常是基于模拟和/或重采样的，因此需要计算。一方面，我们已经能够在理论上证明大约20年前提出的方法是合理的，另一方面，我们已经能够成功地将该方法应用于一项关于怀孕期间药物安全性的研究。在接下来的研究中，我们将研究需要复杂的多状态模型的研究的计划和统计推断的中心问题。特别是，我们将专注于所谓的暴露密度抽样，作为一种有效的替代方案，用于很少接触风险因素的研究。基于重抽样/置换的统计方法是统计推断的核心。计划中的合作应确保这两个同等重要的方面尽可能紧密地联系在一起。