Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uchanganuzi wa Kunusurika kwa Matukio Yanayoshindana× | Uchanganuzi wa Uhai wa Bayesian× | |
|---|---|---|
| Nyanja≠ | Uchanganuzi wa Uhai | Mbinu za Bayes |
| Familia≠ | Survival analysis | Bayesian methods |
| Mwaka wa asili≠ | 1999 | 2001 |
| Mwanzilishi≠ | Fine, J.P. & Gray, R.J. | Ibrahim, Chen & Sinha |
| Aina≠ | Competing risks survival model | Bayesian time-to-event model |
| Chanzo asilia≠ | 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 ↗ | Ibrahim, J.G., Chen, M.-H. & Sinha, D. (2001). Bayesian Survival Analysis. Springer. DOI ↗ |
| Majina mbadala≠ | Rekabet Eden Riskler Analizi, cumulative incidence function, CIF analysis, cause-specific survival analysis | bayesian sağkalım analizi, bayesian time-to-event analysis, bayesian hazard model |
| Zinazohusiana≠ | 5 | 4 |
| Muhtasari≠ | Competing risks analysis, formalized by Fine and Gray in 1999, is a survival analysis framework for settings where a subject can experience one of several mutually exclusive event types. The key quantity is the cumulative incidence function (CIF), which estimates the probability of a specific event occurring by time t in the presence of the other competing events. | Bayesian survival analysis applies Bayesian inference to time-to-event models — Cox proportional hazards, parametric (Weibull, exponential), and cure models. Formalised comprehensively by Ibrahim, Chen and Sinha (2001), the approach encodes prior knowledge about hazard rates and regression coefficients, then updates it with censored survival data to yield posterior hazard ratios and credible intervals rather than single point estimates. |
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