Statistical Estimates from Black Non-Hispanic Female Breast Cancer Data

Khan, Hafiz Mohammad Rafiqullah; Ibrahimou, Boubakari; Saxena, Anshul; Gabbidon, Kemesha; Abdool-Ghany, Faheema; Ramamoorthy, Venkataraghavan; Ullah, Duff; Stewart, Tiffanie Shauna-Jeanne

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저자정보: Khan, Hafiz Mohammad Rafiqullah (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) Ibrahimou, Boubakari (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) Saxena, Anshul (Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University) Gabbidon, Kemesha (Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University) Abdool-Ghany, Faheema (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) Ramamoorthy, Venkataraghavan (Department of Dietetics and Nutrition, Robert Stempel College of Public Health and Social Work, Florida International University) Ullah, Duff (Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University) Stewart, Tiffanie Shauna-Jeanne (Department of Dietetics and Nutrition, Robert Stempel College of Public Health and Social W)

저널정보: 아시아태평양암예방학회 Asian Pacific journal of cancer prevention : APJCP Asian Pacific journal of cancer prevention : APJCP 제15권 제19호

발행연도: 2014.1

수록면: 8,371 - 8,376 (6page)

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Background: The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and Methods: We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model. Results: We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained. Conclusions: These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.

#Model development #Bayesian method #statistical inference #breast cancer survival #black non-Hispanic

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Khan, Hafiz Mohammad Rafiqullah

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