Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Risk-adjusted Phase IV study× | Viktning med inversa sannolikheter för behandling (IPW / IPTW)× | |
|---|---|---|
| Ämnesområde≠ | Epidemiologi | Kausal inferens |
| Familj≠ | Process / pipeline | Regression model |
| Ursprungsår≠ | 1990s–2000s (formalized with ICH E2E and EMA PASS guidelines) | 2000 |
| Upphovsperson≠ | Regulatory and pharmacoepidemiology community (ICH, EMA, FDA frameworks) | Robins, Hernán & Brumback |
| Typ≠ | Observational / quasi-experimental clinical study design | Causal inference weighting estimator |
| Ursprungskälla≠ | Strom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). John Wiley & Sons. ISBN: 978-0470863107 | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias≠ | risk-adjusted post-marketing surveillance study, adjusted Phase IV trial, risk-stratified post-authorization study, PASS with risk adjustment | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Närliggande≠ | 3 | 5 |
| Sammanfattning≠ | A risk-adjusted Phase IV study is an observational or semi-experimental post-marketing study conducted after a drug or device has received regulatory approval. It uses statistical risk-adjustment techniques — such as propensity score matching, inverse probability weighting, or multivariable regression — to control for confounding by indication and baseline patient differences, thereby producing more credible safety, effectiveness, and utilization estimates than unadjusted real-world analyses. | 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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