Date of Award
Open Access Dissertation
Epidemiology and Biostatistics
Modern medical treatments have substantially improved cure rates for many chronic diseases and have generated increasing interest in appropriate statistical models to handle survival data with non-negligible cure fractions. The mixture cure models are designed to model such data set, which assume that studied population is a mixture of being cured and uncured. In this dissertation, I will develop two programs named smcure and NPHMC in R. The first program aims to facilitate estimating two popular mixture cure models: the proportional hazards (PH) mixture cure model and accelerated failure time (AFT) mixture cure model. The second program focuses on designing the sample size needed in survival trial with and without cure fractions based on the PH mixture cure model and standard PH model. The two programs have been tested by comprehensive settings and real data analysis and are now available for download from R CRAN. The third project in my dissertation will focus on the development of a new estimation method for the PH mixture cure model with competing risk data. The performance of proposed method has been evaluated by extensive simulation studies.
Cai, C.(2013). Advanced Methodology Developments in Mixture Cure Models. (Doctoral dissertation). Retrieved from http://scholarcommons.sc.edu/etd/544