Background: Statistical methods are very important to precisely measure breast cancer patient survivaltimes for healthcare management. Previous studies considered basic statistics to measure survival times withoutincorporating statistical modeling strategies. The objective of this study was to develop a data-based statisticalprobability model from the female breast cancer patients’ survival times by using the Bayesian approach topredict future inferences of survival times. Materials and
Methods: A random sample of 500 female patients wasselected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, thestandard model building criteria were used. The Bayesian approach is used to obtain the predictive survival timesfrom the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtainthe summary results for predictive inference.
Results: The highest number of female breast cancer patients wasfound in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age atdiagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33(83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to theExponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survivaltimes.
Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers andproviders to precisely predict future survival estimates as the recent growing challenges of analyzing healthcaredata have created new demand for model-based survival estimates. The application of Bayesian will produceprecise estimates of future survival times.