Model-Based Survival Estimates of Female Breast Cancer Data

Khan, Hafiz Mohammad Rafiqullah; Saxena, Anshul; Gabbidon, Kemesha; Rana, Sagar; Ahmed, Nasar Uddin

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저자정보: Khan, Hafiz Mohammad Rafiqullah (Department of Biostatistics, Robert Stempel College of Public Health & Social Work, Florida International University) Saxena, Anshul (Department of Health Promotion & Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University) Gabbidon, Kemesha (Department of Health Promotion & Disease Prevention, Robert Stempel College of Public Health & Social Work, Florida International University) Rana, Sagar (Division of Research Information and Data Coordinating Center, Florida International University) Ahmed, Nasar Uddin (Department of Epidemiology, Robert Stempel College of Public Health & Social Work, Florida International University)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제15권 제6호

발행연도: 2014.1

수록면: 2,893 - 2,900 (8page)

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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. Materials and 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.

#Bayesian method #model development #predictive inference

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Khan, Hafiz Mohammad Rafiqullah

소속기관 Department of Biostatistics, Robert Stempel College of Public Health & Social Work Florida Inter