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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Kerman University of Medical Sciences
- نوع مقاله: Journal Article
- کلمات کلیدی: dialysis,End Stage Renal Disease,Random Survival Forest Model,Events per Variable
- چکیده:
- چکیده انگلیسی: Background:Dialysis is a process for eliminating extra uremic fluids of patients with chronic renal failure. The present study aimed to determine the variables that influence the survival of dialysis patients using random survival forest model (RSFM) in low-dimensional data with low events per variable (EPV).
Methods:In this historical cohort study, information was collected from 252 dialysis patients in Bandar Abbas hospitals, Iran. The survival time of the patients was calculated in years from the onset of dialysis to death or until the end of the study in 2016. RSFM was used as the number of events per variable (EPV) was low. The data collected from 252 patients were randomly divided into training and testing sets, and this process was repeated 100 times. C-index and Brier Score (BS) were used to assess the performance of the model in the test set.
Results: In this study, 35 (13.9%) mortality cases were observed. Based on the findings, the mean C-index value in training and testing sets was 0.640 and 0.687, and the mean BS value in training and testing sets was 0.017 and 0.023, respectively. The results of the RSFM revealed that BMI, education, occupation, dialysis duration, number of dialysis sessions and age at dialysis onset were the most important factors.
Conclusion: RSFM can be used to determine the survival of dialysis patients and manage low-dimensional data with few-events if the researcher desires to select a nonparametric model.- انتشار مقاله: 11-04-1398
- نویسندگان: Shideh Rafati,Mohammad Reza Baneshi,Laleh Hassani,Abbas Bahrampour
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Breast cancer,Survival,Cure models,Bayesian
- چکیده:
- چکیده انگلیسی: Background: Breast cancer is a top biomedical research priority, and it is a major health problem. Therefore, the present study aimed to determine the prognostic factors of breast cancer survival using cure models. Methods: In this retrospective cohort analytic study, data of 140 breast cancer patients were collected from Ali Ibn Abi Taleb hospital, Rafsanjan, Southeastern Iran. Since in this study, a part of the population had long-term survival, cure models were used and evaluated using DIC index. The data were analyzed using Openbugs Software. Results: In this study, of 140 breast cancer patients, 23 (16.4%) cases died of breast cancer. Based on the findings, the Bayesian nonmixture cure model, with type I Dagum distribution, was the best fitted model. The variables of BMI, number of children, number of natural deliveries, tumor size, metastasis, consumption of canned food, tobacco use, and breastfeeding affected patients’ survival based on type I Dagum distribution. Conclusion: The results of the present study demonstrated that the Bayesian nonmixture cure model, with type I Dagum distribution, can be a good model to determine factors affecting the survival of patients when there is the possibility of a fraction of cure. In this study, it was found that adapting a healthy lifestyle (eg, avoiding canned foods and smoking) can improve the survival of breast cancer patients.
- انتشار مقاله: 07-07-1398
- نویسندگان: Shideh Rafati,Mohammad Reza Baneshi,Abbas Bahrampour
- مشاهده