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| 生存回归× | Kaplan-Meier生存估计量× | 威布尔参数生存回归× | |
|---|---|---|---|
| 领域≠ | 统计学 | 生存分析 | 生存分析 |
| 方法族≠ | Regression model | Survival analysis | Survival analysis |
| 起源年份≠ | 1980s | 1958 | 1951 |
| 提出者≠ | Kalbfleisch & Prentice; Cox & Oakes | Kaplan, E. L. & Meier, P. | Waloddi Weibull |
| 类型≠ | Parametric survival model | Non-parametric survival estimator | Fully parametric survival regression model |
| 开创性文献≠ | 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 ↗ | Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. DOI ↗ |
| 别名≠ | accelerated failure time model, AFT model, parametric survival model, time-to-event regression | product-limit estimator, km curve, kaplan-meier sağkalım analizi | weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma |
| 相关≠ | 3 | 2 | 4 |
| 摘要≠ | 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. | Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival. |
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