Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Konkurējošo risku izdzīvošanas analīze× | Kaplana-Meiera izdzīvošanas novērtētājs× | |
|---|---|---|
| Nozare | Dzīvildze | Dzīvildze |
| Saime | Survival analysis | Survival analysis |
| Izcelsmes gads≠ | 1999 | 1958 |
| Autors≠ | Fine, J.P. & Gray, R.J. | Kaplan, E. L. & Meier, P. |
| Tips≠ | Competing risks survival model | Non-parametric survival estimator |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | Rekabet Eden Riskler Analizi, cumulative incidence function, CIF analysis, cause-specific survival analysis | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Saistītās≠ | 5 | 2 |
| Kopsavilkums≠ | 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. | The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups. |
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