পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| রেট্রোস্পেক্টিভ কম্পিটিং রিস্কস অ্যানালাইসিস× | কপ্ল্যান-মেয়ার বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র | মহামারীবিদ্যা | মহামারীবিদ্যা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1978 (cause-specific); 1999 (subdistribution/Fine-Gray) | 1958 |
| প্রবর্তক≠ | Fine & Gray (subdistribution model); Prentice et al. (cause-specific framework) | Edward L. Kaplan and Paul Meier |
| ধরন≠ | Retrospective observational survival analysis | Nonparametric survival estimator |
| মৌলিক উৎস≠ | Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| অপর নাম | retrospective CRA, competing risks survival analysis (retrospective), cause-specific hazard analysis (retrospective), subdistribution hazard analysis (retrospective) | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| সম্পর্কিত≠ | 4 | 5 |
| সারসংক্ষেপ≠ | Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the probability of each event type while accounting for the fact that occurrence of one event permanently precludes the others. Widely used in oncology, cardiology, and transplant medicine where administrative or registry records are the data source. | Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored observations — participants who left the study or had not yet experienced the event by the end of follow-up. It is one of the most widely used techniques in clinical and epidemiological research. |
| ScholarGateডেটাসেট ↗ |
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