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Abstract:
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In many medical and health-care contexts, a failure event (such as death, hospitalization or transplant) is triggered when a subject's deteriorating health first reaches a failure threshold. The failure process is well described as the sample path of a stochastic process hitting a boundary. The parameters and behaviors of such failure processes must often be inferred from data sets that include censored survival times and current health levels of survivors. A substantial input of expert experience with the health context is usually required to guide the data modeling. This talk describes a parsimonious model for the failure process that has only one distributional property, namely, stationary independent increments. As this property is frequently encountered in real applications, the stochastic model and its related statistical methodology have potential for general application in many fields. The mathematical underpinnings of the distribution-free methods for estimation and prediction will be described in the talk as well as techniques for incorporating covariates. Case examples will be presented.
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