Survival and Reliability Models with High Dimensional Data

该建议涉及新的方法和模型来描述事件数据的时间。应用领域包括工程可靠性、医疗统计等。例如，在医学统计学中，临床试验数据模型（包括艰难梭菌患者治愈时间的估计）是当前感兴趣的。在工程中，主要裂纹发展到临界尺寸导致由于金属疲劳而失效的时间也是令人感兴趣的。我对失败或事件时间模型特别感兴趣，这些模型基于过程的合理基础模型，实际上会产生失败。事件时间数据最流行的模型是考克斯模型。虽然这是非常有用的，但考克斯本人对它可能被过度使用表示怀疑（Statistical Science，Reid 1994）。这个模型和更一般的计数过程模型的一个重要方面是，它们专注于风险函数的纯经验模型。早期的研究，包括我自己的一些研究，在工程可靠性领域，表明相当多的主题知识，例如，由于金属疲劳失效，通常更有说服力的应用科学家。

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.