Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на преживяемостта, коригиран спрямо риска× | Анализ на Каплан-Майер× | |
|---|---|---|
| Област | Епидемиология | Епидемиология |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1972 (Cox regression); broader covariate-adjusted survival methods developed 1970s–1990s | 1958 |
| Създател≠ | D. R. Cox (regression framework); extensions via Kaplan & Meier, Breslow, and others | Edward L. Kaplan and Paul Meier |
| Тип≠ | Observational and experimental analytical method | Nonparametric survival estimator |
| Основополагащ източник≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. link ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Други названия | covariate-adjusted survival analysis, adjusted time-to-event analysis, risk-stratified survival analysis, adjusted Kaplan-Meier / Cox analysis | KM analysis, KM estimator, product-limit estimator, Kaplan-Meier curve |
| Свързани | 5 | 5 |
| Резюме≠ | Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazard ratios, survival curves, and median survival estimates that are attributable to the factor of interest rather than to pre-existing risk differences between groups. | 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. |
| ScholarGateНабор от данни ↗ |
|
|