Model-based survival estimates of female breast cancer data.
BACKGROUND
Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times.


METHODS
A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference.


RESULTS
The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (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 the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times.


CONCLUSIONS
The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.
Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, USA E-mail : hmkhan@fiu.edu.
Hafiz Mohammad Rafiqullah
HM
Khan
Anshul
A
Saxena
Kemesha
K
Gabbidon
Sagar
S
Rana
Nasar Uddin
NU
Ahmed
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000401:mortality*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008390:Markov Chains; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D011379:Prognosis; D018426:SEER Program; D015996:Survival Rate