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| Adaptiv Fas IV-studie× | Bayesiansk fas IV-studie× | |
|---|---|---|
| Ämnesområde | Epidemiologi | Epidemiologi |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1990s–2000s (regulatory formalization of adaptive Phase IV designs) | 1980s–1990s (formalized application to post-marketing settings) |
| Upphovsperson≠ | Adaptive design principles developed by multiple statisticians; Phase IV framework established by regulatory bodies (FDA, EMA) in the late 20th century | Donald A. Berry and colleagues (applied Bayesian framework to clinical trials) |
| Typ≠ | Adaptive post-marketing clinical study design | Observational or interventional post-marketing study with Bayesian inference |
| Ursprungskälla≠ | Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584889625 | Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756 |
| Alias | adaptive post-marketing surveillance study, adaptive pharmacovigilance study, adaptive Phase IV trial, adaptive post-approval study | Bayesian post-marketing surveillance study, Bayesian pharmacovigilance study, Bayesian post-approval study, Bayesian phase 4 trial |
| Närliggande≠ | 6 | 0 |
| Sammanfattning≠ | An Adaptive Phase IV study is a post-marketing surveillance study conducted after a drug or intervention has received regulatory approval, augmented with pre-specified adaptive design elements that allow pre-planned modifications to the study protocol in response to accumulating data. These modifications may include sample size re-estimation, endpoint adjustments, or population enrichment, all governed by statistical rules set before the study begins, preserving scientific integrity while increasing efficiency. | A Bayesian Phase IV study is a post-marketing research design that applies Bayesian statistical inference to accumulate evidence about a drug or device already approved for clinical use. By formally combining prior evidence from earlier development phases with emerging real-world data, it enables continuous, probabilistic updating of safety and effectiveness estimates — moving beyond the binary hypothesis tests of conventional frequentist surveillance. |
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