Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Regresioni Cox Proporcional i Përshtatur× | Analiza e mbijetesës× | |
|---|---|---|
| Fusha≠ | Epidemiologji | Statistika e hulumtimit |
| Familja | Process / pipeline | Process / pipeline |
| Viti i origjinës≠ | 1972 (Cox model); matched extension widely adopted 1970s–1980s | 1958 |
| Krijuesi≠ | D. R. Cox (Cox model, 1972); stratification extension for matched designs by subsequent methodologists including D. C. Thomas | Edward L. Kaplan and Paul Meier |
| Lloji≠ | Semi-parametric survival regression for matched data | Method |
| Burimi themelues≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ | Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗ |
| Emërtime të tjera≠ | stratified Cox regression, conditional Cox model, matched survival analysis, Cox model for matched pairs | Kaplan-Meier analysis, Cox regression, TTE analysis |
| Të lidhura≠ | 4 | 3 |
| Përmbledhja≠ | Matched Cox proportional hazards is a survival analysis method that extends the Cox regression model to appropriately handle data arising from matched study designs — matched cohorts or matched case-control studies with time-to-event outcomes. By stratifying the partial likelihood by matched set, the method eliminates confounding from matching factors without estimating their baseline hazard, yielding valid hazard ratio estimates that are free from matching-induced bias. | 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. |
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