Σύγκριση μεθόδων

	Προσαρμοσμένη Εσωτερική Μελέτη Περίπτωσης-Ελέγχου	Αντιστοίχιση Βαθμολογίας Προδιάθεσης
Πεδίο	Επιδημιολογία	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1977 (nested case-control); risk-adjusted extensions 1980s–2000s	1983
Δημιουργός	Thomas (1977) for nested case-control; risk adjustment extensions developed through pharmacoepidemiology literature (1980s–2000s)	Paul Rosenbaum and Donald Rubin
Τύπος	Observational analytical study design	Method
Θεμελιώδης πηγή	Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Εναλλακτικές ονομασίες	risk-adjusted NCC, covariate-adjusted nested case-control, propensity-score nested case-control, nested case-control with risk adjustment	PSM, propensity score weighting, covariate balance
Συναφείς	4	3
Σύνοψη	A risk-adjusted nested case-control study embeds a case-control comparison inside a defined cohort and explicitly accounts for differences in baseline risk between cases and controls through covariate adjustment — most commonly via risk scores, propensity scores, or stratification. It preserves the efficiency advantages of the nested design while reducing confounding attributable to pre-existing risk differentials, making it especially valuable in pharmacoepidemiology and clinical effectiveness research.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.