A finite mixture survival model to characterize risk groups of neuroblastoma.

Neuroblastoma is a childhood cancer with patients experiencing heterogeneous survival outcomes despite aggressive treatment. Disease outcomes range from early death to spontaneous regression of the tumor followed by cure. Owing to this heterogeneity, it is of interest to identify patients with similar types of neuroblastoma so that specific types of treatment can be developed. Oncologists are especially interested in identifying patients who will be cured so that the minimum amount of a potentially toxic treatment can be given to this group of patients. We analyze a large cohort of neuroblastoma patients and develop a finite mixture model that uses covariates to predict the probability of being in a cure group or other (one or more) risk groups. A prediction method is developed that uses the estimated probabilities to assign a patient to different risk groups. The robustness of the model and the prediction method is examined via simulation by looking at misclassification rates under misspecified models.