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| Анализ на преживяемостта, коригиран спрямо риска× | Анализ на преживяемостта× | |
|---|---|---|
| Област≠ | Епидемиология | Статистика за изследвания |
| 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 | Method |
| 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 | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Свързани≠ | 5 | 3 |
| 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. | Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters. |
| ScholarGateНабор от данни ↗ |
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