Comparison of Bayesian survival analysis and Cox regression analysis in simulated and breast cancer data sets |
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| Authors: | Imran Kurt Omurlu Kazim Ozdamar Mevlut Ture |
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| Affiliation: | 1. Trakya University Medical Faculty, Department of Biostatistics, 22030 Edirne, Turkey;2. Eski?ehir Osmangazi University Medical Faculty, Department of Biostatistics, Eski?ehir, Turkey;3. Adnan Menderes University Medical Faculty, Department of Biostatistics, Ayd?n, Turkey;1. Mobility Program Clinical Research Unit, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada;2. Institute of Health Policy, Management & Evaluation, University of Toronto, Suite 425, 155 College Street, Toronto, Ontario M5T 3M7, Canada;3. Mobility Program, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada;4. Department of Surgery, University of Toronto, 149 College Street, 5th Floor, Toronto, Ontario M5T 1P5, Canada;1. Department of Biomedical Statistics and Informatics, Mayo Clinic, Scottsdale, Arizona;2. Department of Radiation Oncology, Washington University in St Louis School of Medicine, St Louis, Missouri;3. Department of Radiation Oncology, Summa Akron City Hospital, Akron, Ohio;1. Department of Statistics, The Ohio State University, Columbus, OH, 43210, USA;2. Department of Statistics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;1. Centro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy), MDE-UPCT, Calle Coronel Lopez Peña, s/n, 30720 Santiago de la Ribera, Murcia, Spain;2. Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal;3. Centre for Informatics and Systems, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal;4. Portuguese Institute of Oncology of Porto, Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto, Portugal;1. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA;2. Department of Computer Science, University of Southern California, Los Angeles, CA;3. Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA |
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| Abstract: | We aimed to compare the performance of Cox regression analysis (CRA) and Bayesian survival analysis (BSA) by using simulations and breast cancer data.Simulation study was carried out with two different algorithms that were informative and noninformative priors. Moreover, in a real data set application, breast cancer data set related to disease-free survival (DFS) that was obtained from 423 breast cancer patients diagnosed between 1998 and 2007 was used.In the simulation application, it was observed that BSA with noninformative priors and CRA methods showed similar performances in point of convergence to simulation parameter. In the informative priors’ simulation application, BSA with proper informative prior showed a good performance with too little bias. It was found out that the bias of BSA increased while priors were becoming distant from reliability in all sample sizes. In addition, BSA obtained predictions with more little bias and standard error than the CRA in both small and big samples in the light of proper priors.In the breast cancer data set, age, tumor size, hormonal therapy, and axillary nodal status were found statistically significant prognostic factors for DFS in stepwise CRA and BSA with informative and noninformative priors. Furthermore, standard errors of predictions in BSA with informative priors were observed slightly.As a result, BSA showed better performance than CRA, when subjective data analysis was performed by considering expert opinions and historical knowledge about parameters. Consequently, BSA should be preferred in existence of reliable informative priors, in the contrast cases, CRA should be preferred. |
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