by Binbing Yu and Ram C. Tiwari

Journal of Data Science, v.5, no.1, 41-51

Abstract

The interest in estimating the probability of cure has been increasing in cancer survival analysis as the cure of some cancer sites is becoming a reality. Mixture cure models have been used to model the failure time data with the existence of long-term survivors. The mixture cure model assumes that a fraction of the survivors are cured from the disease of interest. The failure time distribution for the uncured individuals (latency) can be modeled by either parametric models or a semi-parametric proportiona l hazards model. In the model, the probability of cure and the latency distribution are both related to the prognostic factors and patients' characteristics. The maximum likelihood estimates (MLEs) of these parameters can be obtained using the Newton-Raphson algorithm. The EM algorithm has been proposed as a simple alternative by Larson and Dinse (1985) and Taylor (1995). in various setting for the cause-specific survival analysis. This approach is extended here to the grouped relative survival data. The methods are applied to analyze the colorectal cancer relative survival data from the Surveillance, Epidemiology, and End Results (SEER) program.

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