The University of Arizona

A unified framework for fitting Bayesian semiparametric models to arbitrarily censored spatial survival data

A unified framework for fitting Bayesian semiparametric models to arbitrarily censored spatial survival data

Series: Statistics GIDP Colloquium
Location: Math 501
Presenter: Timothy Hanson, Professor, Department of Statistics, University of South Carolina

A comprehensive, unified approach to modeling arbitrarily censored spatial survival data is presented for the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. Unlike many other approaches, all manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left truncated data are also accommodated leading to models for time-dependent covariates.  Both georeferenced (location observed exactly) and areally observed (location known up to a geographic unit such as a county) spatial locations are handled. Variable selection is also incorporated.  Model fit is assessed with conditional Cox-Snell residuals, and model choice carried out via LPML and DIC.  Baseline survival is modeled with a novel transformed Bernstein polynomial prior. All models are fit via new functions which call efficient compiled C++ in the R package spBayesSurv. The methodology is broadly illustrated with simulations and real data applications.  An important finding is that proportional odds and accelerated failure time models often fit significantly better than the commonly-used proportional hazards model.

(Refreshments will be served.)

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