Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regresija izdzīvošanai× | Kaplana-Meiera izdzīvošanas novērtētājs× | |
|---|---|---|
| Nozare≠ | Statistika | Dzīvildze |
| Saime≠ | Regression model | Survival analysis |
| Izcelsmes gads≠ | 1980s | 1958 |
| Autors≠ | Kalbfleisch & Prentice; Cox & Oakes | Kaplan, E. L. & Meier, P. |
| Tips≠ | Parametric survival model | Non-parametric survival estimator |
| Pirmavots≠ | Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576 | Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Citi nosaukumi≠ | accelerated failure time model, AFT model, parametric survival model, time-to-event regression | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Saistītās≠ | 3 | 2 |
| Kopsavilkums≠ | Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood. | 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. |
| ScholarGateDatu kopa ↗ |
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