Survival and Reliability Models with High Dimensional Data

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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.