Abstract:
Survival analysis methods that measure the risk of death or progression of a disease provide predictions that help clinicians to estimate trends in their patient outcomes. The objective of the study was to determine the survival pattern of breast cancer patients, using the parametric modeling strategies. Five parametric models-exponential, Weibull, Lognormal Gamma and Llogistic—were applied to the real life data which consisted of 1022 women diagnosed with breast cancer between 1 st January 2002 and 31st December 2008. Survival time was calculated from the date of the diagnosis of breast cancer to the date of death or, if alive, at 31 December 2011. Using the log likelihood method and the Akaike information criterion (AIC) the gamma model was found to be the best-fitted model for predicting survival following a diagnosis of breast cancer. Several covariates-including size of tumour, tumour grade; stage at diagnosis; axillary node involvement; Body Mass Index (BMI) and Age (age of the patient in years)—were included in the parametric model to predict factors associated with future mortality. Size of tumour, stage at diagnosis and Body Mass Index (BMI) were found to be significant variables associated with mortality of breast cancer patients
