Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gekoppelde Fase IV-studie× | Propensity Score Matching× | |
|---|---|---|
| Vakgebied≠ | Epidemiologie | Onderzoeksstatistiek |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1980s–1990s (formalized in post-marketing regulatory frameworks) | 1983 |
| Grondlegger≠ | Regulatory tradition (FDA, EMA); matching methodology from Rosenbaum & Rubin (1983) | Paul Rosenbaum and Donald Rubin |
| Type≠ | Observational study design | Method |
| Oorspronkelijke bron≠ | Strom, B. L., & Kimmel, S. E. (Eds.). (2005). Textbook of Pharmacoepidemiology. Wiley. ISBN: 978-0470029244 | 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 ↗ |
| Aliassen≠ | matched post-marketing surveillance study, Phase IV matched cohort study, matched pharmacoepidemiological study, post-authorization matched safety study | PSM, propensity score weighting, covariate balance |
| Verwant≠ | 5 | 3 |
| Samenvatting≠ | A Matched Phase IV study is a post-marketing observational design in which patients who received an approved drug (or intervention) are matched to comparable non-exposed patients — or patients on an alternative therapy — to evaluate real-world safety, effectiveness, or long-term outcomes. Conducted after regulatory approval, it combines the epidemiological rigour of matching with the breadth of post-authorization pharmacovigilance, generating evidence that randomized trials are rarely powered or timed to provide. | 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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