Survival and Reliability Models with High Dimensional Data

这项建议涉及描述事件间隔时间数据的新方法和模型。应用领域包括工程可靠性、医疗统计和其他。例如，在医学统计学中，临床试验数据的模型，包括对C.Difficile患者治愈时间的估计，是目前感兴趣的。在工程中，主要裂纹发展到临界尺寸并导致金属疲劳失效的时间也是令人感兴趣的。我对故障或事件时间模型特别感兴趣，这些模型基于看似合理的流程底层模型，而该流程实际上会产生故障。最流行的事件时间数据模型是考克斯模型。虽然这非常有用，但考克斯本人对它可能被过度使用表示怀疑(《统计科学》，里德1994)。这个模型的一个重要方面，以及更一般的计数过程模型，是它们专注于风险函数的纯经验模型。早期的研究，包括我自己在工程可靠性领域的一些研究表明，对应用科学家来说，相当多的主题知识，例如金属疲劳导致的故障，通常更有说服力。 我考虑了几个模型，它们都有一个基于流程的基础。该过程可以在工程环境中测量累积损害，或者在医学环境中测量人类健康的假定测量。Birnbaum-Saunders疲劳寿命模型源于主导裂纹扩展的随机模型，最终导致灾难性破坏。它与一类被称为首次命中时间(FHT)的模型有关。在这种情况下，潜在的过程可能是Wiener过程(见Lee和Whitmore，2006)。我建议使用基于Wiener过程的模型的变体，以及Birnbaum-Saunders模型。我将开发新的方法来处理这两个模型的高维(HD)协变量数据；一个例子是微生物组数据，其中协变量空间与临床试验参与者的高维下一代测序数据相关。我要研究的另一个问题是这些模型的随机效应版本。这可以用于聚集数据，或者作为COX PH乘性脆弱性模型的替代。后一种模型有缺陷，因为PH假设可能失败，而且乘性脆弱性也不完全令人信服，Aalen等人(2008)。我将为基于FHTs的各种治愈率模型开发新的方法。相似模型在工程可靠性中非常有用。这些阈值模型是一个令人兴奋的替代模型，例如经常在医学统计学中使用的经过充分研究的Cox模型，或在工程可靠性中流行的加速失效时间模型。新方法的发展和这些新模型在实际科学和技术问题上的应用有很大的空间。与更传统的建模方法相比，这是一个非常令人兴奋的替代范例。

This proposal deals with novel methods and models to describe time to event data. Areas of application include engineering reliability, medical statistics and others. For example, in medical statistics, models for clinical trial data, which include estimation of time to cure of patients with C.Difficile is of current interest. In engineering the time for a dominant crack to develop to a critical size resulting in failure due to metal fatigue is also of interest. I am particularly interested in failure or event time models, which are based on a plausible underlying model of the process, which actually produces failure. The most popular model for event time data is the Cox model. While this has been very useful, Cox himself has expressed doubts about its possible overuse (Statistical Science, Reid 1994). An important aspect of this model, and more general counting process models, is that they focus on purely empirical models of the hazard function. Earlier research, including some of my own, in the engineering reliability area, suggests that considerable subject matter knowledge in, for example, failure due to metal fatigue, is usually more convincing to applied scientists. I consider several models, which have an underlying process-based origin. The process may measure cumulative damage in an engineering context or a putative measure of human health in a medical context. The Birnbaum-Saunders fatigue life model arises from a stochastic model of the growth of a dominant crack eventually leading to catastrophic failure. It is related to a class of models referred to as First Hitting Time (FHT). The underlying process in this case may be a Wiener process (see Lee and Whitmore 2006). I propose to work with variants of Wiener process based models, as well as the Birnbaum-Saunders model. I will develop novel methods to deal with high-dimensional (HD) covariate data for both of these models; an example is microbiome data, in which the covariate space relates to very HD next generation sequencing data on clinical trial participants. Another issue I will study is random effects versions of these models.This can be used for clustered data or as an alternative to multiplicative frailty models for Cox PH. The latter model has flaws, in that the PH assumption may fail and also multiplicative frailty is not entirely convincing, Aalen et al (2008). I will develop novel methods for a variety of cure rate models based on FHTs. Similar models are very useful in engineering reliability. These threshold models are an exciting alternative to models such as the well-studied Cox model, frequently used in medical statistics, or the accelerated failure time model, which is popular in engineering reliability. There is great scope for new methodological development and applications of these new models to real scientific and technological problems. This is a very exciting alternative paradigm to more conventional modeling approaches.