Date of Award
Open Access Thesis
Epidemiology and Biostatistics
Typical survival methods have the assumption that every subject will eventually experience the event of interest, given enough follow-up time. However, there are some occasions in which a proportion of the population of interest will never experience the event of interest. Therefore, the incorporation of a “cure” fraction in a statistical model is necessary. In this thesis, I comprehensively evaluate mixture cure models in two different statistical software programs: the smcure package in R and the PSPMCM macro in SAS. Extensive simulation studies in R and SAS allow evaluation of the performance of these two models. An additional aspect of this thesis involves application of the mixture cure models in R and SAS to a new real data set of soft tissue sarcoma patients. The results from the models fitted to the sarcoma data set in R and in SAS will then be compared.
Robinson, M.(2014). Mixture Cure Models: Simulation Comparisons of Methods in R and SAS. (Master's thesis). Retrieved from http://scholarcommons.sc.edu/etd/2934