Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Étude de cohorte avec ajustement des risques× | Appariement par score de propension× | |
|---|---|---|
| Domaine≠ | Épidémiologie | Statistiques de recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | Mid–late 20th century (risk-adjusted cohort designs systematized by 1970s–1990s) | 1983 |
| Auteur d'origine≠ | Evolution of cohort study methodology; risk adjustment formalized through work of Rothman, Greenland, and others in epidemiology, 20th century | Paul Rosenbaum and Donald Rubin |
| Type≠ | Observational epidemiological study design with statistical confounding control | Method |
| Source fondatrice≠ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 | 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. DOI ↗ |
| Alias≠ | adjusted cohort study, covariate-adjusted cohort, risk-controlled prospective study, propensity-adjusted cohort | PSM, propensity score weighting, covariate balance |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | A risk-adjusted cohort study is an observational epidemiological design in which a defined group of individuals is followed over time to compare outcomes between exposed and unexposed subgroups, with statistical methods applied to control for measured confounders. Adjustment strategies — including multivariable regression, propensity score matching, inverse probability weighting, or standardization — are used to reduce bias and produce effect estimates that more closely approximate what would be observed in a randomized trial. | 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. |
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