Department of Statistics, Faculty of Mathematical Sciences, University of Delhi, India
Research Article
On the Estimation of Cure Rate in the Presence of Prognostic Factors using Various Discrete Count Distributions
Author(s): Seema Pant, Manoj Kumar Varshney, Gurprit Grover and Seema Pant*
Background: Owing to the new treatments and medicines, many cancer patients get cured of the disease and they do not experience the event of interest (death). Such patients constitute the cure fraction. To analyze survival data related to diseases with cured fraction, cure rate models have been found to be more appropriate as compared to the standard survival models. Promotional Time Cure Rate Model is one such model and it assumes that the patient death may have been caused due to some latent competing causes. In our case we have assumed that the number of competing causes follow either Binomial or Poisson or Negative Binomial Distribution.
Material and Methods: Parameter estimation has been done by Bayesian approach, using Markov Chain Monte Carlo (MCMC) technique. A real dataset from a breast cancer data of 85 patients is used to illustra.. View More»
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