Analysis of cure rate survival data under proportional odds model.
Due to significant progress in cancer treatments and management in survival studies involving time to relapse (or death), we often need survival models with cured fraction to account for the subjects enjoying prolonged survival. Our article presents a new proportional odds survival models with a cured fraction using a special hierarchical structure of the latent factors activating cure. This new model has same important differences with classical proportional odds survival models and existing cure-rate survival models. We demonstrate the implementation of Bayesian data analysis using our model with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute. Particularly aimed at survival data with cured fraction, we present a novel Bayes method for model comparisons and assessments, and demonstrate our new tool's superior performance and advantages over competing tools.
Gu
Yu
Y
Department of Statistics, Florida State University, Tallahassee, FL 32310-5608, USA. yugu@stat.fsu.edu
Sinha
Debajyoti
D
Banerjee
Sudipto
S
D001499:Bayes Theorem; D018572:Disease-Free Survival; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D054547:National Cancer Institute (U.S.); D009369:Neoplasms / Q000401:mortality* / Q000628:therapy; D016017:Odds Ratio; D016016:Proportional Hazards Models; D018426:SEER Program; D016019:Survival Analysis; D014481:United States