Cure Rate Models
Survival models incorporating a cure fraction, often referred to as cure rate models, are becoming increasingly popular in analyzing data from cancer clinical trials. The cure rate model has been used for modeling time-to-event data for various types of cancers, including breast cancer, non-Hodgkins lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer, where for these diseases, a significant proportion of patients are “cured.” Perhaps the most popular type of cure rate model is the mixture model discussed by Berkson and Gage (1952). In this model, we assume a certain fraction π of the population is “cured,” and the remaining 1 – π are not cured.
KeywordsPosterior Distribution Failure Time Markov Chain Monte Carlo Algorithm High Posterior Density Posterior Estimate
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