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| Dopasowany model proporcjonalnego hazardu Coxa× | Badanie kohortowe z dopasowaniem× | |
|---|---|---|
| Dziedzina | Epidemiologia | Epidemiologia |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1972 (Cox model); matched extension widely adopted 1970s–1980s | Mid-20th century; propensity-score variant 1983 |
| Twórca≠ | D. R. Cox (Cox model, 1972); stratification extension for matched designs by subsequent methodologists including D. C. Thomas | Established practice; propensity-score matching formalized by Rosenbaum & Rubin (1983) |
| Typ≠ | Semi-parametric survival regression for matched data | Observational analytic study design |
| Źródło pierwotne≠ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Inne nazwy | stratified Cox regression, conditional Cox model, matched survival analysis, Cox model for matched pairs | matched follow-up study, paired cohort study, propensity-matched cohort, matched prospective study |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | A matched cohort study is an observational design in which each exposed participant is paired with one or more unexposed counterparts who share key characteristics — such as age, sex, or comorbidity status — before both groups are followed forward in time to compare incident outcomes. Matching controls for measured confounders at the design stage, reducing bias that would otherwise require statistical adjustment alone. |
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