  To address an important risk classification issue that arises in clinical
practice, we propose a new mixture model via latent cure rate markers for
survival data with a cure fraction. In the proposed model, the latent cure rate
markers are modeled via a multinomial logistic regression and patients who
share the same cure rate are classified into the same risk group. Compared to
available cure rate models, the proposed model fits better to data from a
prostate cancer clinical trial. In addition, the proposed model can be used to
determine the number of risk groups and to develop a predictive classification
algorithm.
