Statistical Applications for the Prediction of White Hispanic Breast Cancer Survival

Khan, Hafiz Mohammad Rafiqullah; Saxena, Anshul; Gabbidon, Kemesha; Ross, Elizabeth; Shrestha, Alice

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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) Ross, Elizabeth (Behavioral Science Research Corporation, Coral Gables) Shrestha, Alice (Department of Biostatistics, 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 제14호

발행연도: 2014.1

수록면: 5,571 - 5,575 (5page)

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Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

#Breast cancer survival data #Bayesian inference #statistical models #survival inference

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Khan, Hafiz Mohammad Rafiqullah

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