Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression des risques proportionnels de Cox rétrospective× | Analyse de survie rétrospective× | |
|---|---|---|
| Domaine | Épidémiologie | Épidémiologie |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1972 | 1970s–1980s (retrospective variant established) |
| Auteur d'origine≠ | David R. Cox | Kaplan & Meier (foundational estimator, 1958); Cox (regression model, 1972); retrospective application is a design variant documented since the 1970s |
| Type≠ | Semi-parametric survival regression | Retrospective observational analytical study |
| Source fondatrice≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society, Series B, 34(2), 187–220. DOI ↗ | Collett, D. (2015). Modelling Survival Data in Medical Research (3rd ed.). CRC Press. ISBN: 978-1439856789 |
| Alias | Cox PH regression (retrospective), retrospective Cox survival model, retrospective hazard regression, Cox model on historical data | historical survival study, retrospective time-to-event analysis, retrospective follow-up survival study, archival survival analysis |
| Apparentées | 5 | 5 |
| Résumé≠ | Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline hazard, making it the dominant analytic tool when the investigator works backward from already-recorded outcomes and exposures. | Retrospective survival analysis applies time-to-event statistical methods — most commonly the Kaplan-Meier estimator and Cox proportional hazards regression — to data collected from past records rather than through prospective follow-up. The researcher looks back at medical records, disease registries, or administrative databases to reconstruct each patient's journey from a defined starting point (e.g., diagnosis or surgery) to an outcome of interest (e.g., death, relapse, or hospital readmission), making it a cost-efficient approach for studying prognosis and risk factors when prospective follow-up is not feasible. |
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