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Abstract:
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In head and neck cancer, it is well established that some patients can be cured of disease. We are interested in identifying factors that are associated with the probability of being cured and the times to cancer recurrence, death, and death after recurrence. We propose a multi-state model with a cure structure to model recurrence and death in head and neck cancer. We use Bayesian methods to fit the model and imputation to handle missing covariate data. Due to the large number of covariates and transitions considered, we explore various Bayesian variable selection approaches and model averaging to identify factors that are most strongly associated with the probability of cure and the transition rates to recurrence and death.
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