Cure Rate Models

Abstract

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 1r of the population is "cured," and the remaining 1-1r are not cured. The survivor function for the entire population, denoted by S 1 (t), for this model is given by SI(t) = 1l' + (1-7r)S*(t), (5.1.1) where S*(t) denotes the survivor function for the non-cured group in the population. Common choices for S*(t) are the exponential and Weibull distributions. We shall refer to the model in (5.1.1) as the standard cure rate model. The standard cure rate model has been extensively discussed in the statistical literature by several authors