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| 매칭된 4상 연구× | 역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)× | |
|---|---|---|
| 분야≠ | 역학 | 인과추론 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 1980s–1990s (formalized in post-marketing regulatory frameworks) | 2000 |
| 창시자≠ | Regulatory tradition (FDA, EMA); matching methodology from Rosenbaum & Rubin (1983) | Robins, Hernán & Brumback |
| 유형≠ | Observational study design | Causal inference weighting estimator |
| 원전≠ | Strom, B. L., & Kimmel, S. E. (Eds.). (2005). Textbook of Pharmacoepidemiology. Wiley. ISBN: 978-0470029244 | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 별칭≠ | matched post-marketing surveillance study, Phase IV matched cohort study, matched pharmacoepidemiological study, post-authorization matched safety study | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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