Bayesian Method for Modeling Male Breast Cancer Survival Data
| Autor: | Sagar Rana, Anshul Saxena, Hafiz M. R. Khan, Nasar U. Ahmed |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male Cancer Research Epidemiology Computer science Bayesian inference Breast Neoplasms Male Bayes' theorem Predictive inference Goodness of fit Bayesian information criterion Statistics Statistical inference Humans Survival rate Aged Neoplasm Staging Aged 80 and over Models Statistical Public Health Environmental and Occupational Health Bayes Theorem Middle Aged Prognosis Survival Rate Oncology Akaike information criterion Follow-Up Studies SEER Program |
| Zdroj: | Asian Pacific Journal of Cancer Prevention. 15:663-669 |
| ISSN: | 1513-7368 |
| DOI: | 10.7314/apjcp.2014.15.2.663 |
| Popis: | Background With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and methods A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues. |
| Databáze: | OpenAIRE |
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