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Kockázattal kiigazított IV. fázisú vizsgálat×Az inverz valószínűségi kezelési súlyozás (IPW / IPTW)×
TudományterületEpidemiológiaOksági következtetés
MódszercsaládProcess / pipelineRegression model
Keletkezés éve1990s–2000s (formalized with ICH E2E and EMA PASS guidelines)2000
MegalkotóRegulatory and pharmacoepidemiology community (ICH, EMA, FDA frameworks)Robins, Hernán & Brumback
TípusObservational / quasi-experimental clinical study designCausal inference weighting estimator
AlapműStrom, B. L. (Ed.). (2005). Pharmacoepidemiology (4th ed.). John Wiley & Sons. ISBN: 978-0470863107Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗
Alternatív nevekrisk-adjusted post-marketing surveillance study, adjusted Phase IV trial, risk-stratified post-authorization study, PASS with risk adjustmentIPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Kapcsolódó35
ÖsszefoglalóA 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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ScholarGateMódszerek összehasonlítása: Risk-adjusted Phase IV study · Inverse Probability Weighting. Letöltve 2026-06-19, forrás: https://scholargate.app/hu/compare