Σύγκριση μεθόδων

	Παλινδρόμηση Cox Αναλογικών Κινδύνων για Αντιστοιχισμένα Σχέδια	Μελέτη συζευγμένων συγχρόνων ομάδων
Πεδίο	Επιδημιολογία	Επιδημιολογία
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1972 (Cox model); matched extension widely adopted 1970s–1980s	Mid-20th century; propensity-score variant 1983
Δημιουργός	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)
Τύπος	Semi-parametric survival regression for matched data	Observational analytic study design
Θεμελιώδης πηγή	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Εναλλακτικές ονομασίες	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
Συναφείς	4	5
Σύνοψη	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.