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| Adaptív dózis-válasz analízis× | Dózis-válasz analízis× | |
|---|---|---|
| Tudományterület | Epidemiológia | Epidemiológia |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 2000s (key papers 2005–2007; ICH E4 guidance 1994 for classical dose-response) | Conceptual roots 16th century; modern epidemiological application mid-20th century |
| Megalkotó≠ | Frank Bretz, José Pinheiro and colleagues; foundational MCP-Mod framework | Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill |
| Típus≠ | Adaptive statistical design and analysis | Quantitative analytical method |
| Alapmű≠ | Bretz, F., Pinheiro, J. C., & Branson, M. (2005). Combining multiple comparisons and modeling techniques in dose-response studies. Biometrics, 61(3), 738-748. DOI ↗ | Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641 |
| Alternatív nevek | adaptive DRA, adaptive dose-finding analysis, adaptive exposure-response analysis, adaptive D-R modeling | exposure-response analysis, concentration-response modeling, dose-response modeling, DRA |
| Kapcsolódó≠ | 6 | 4 |
| Összefoglaló≠ | Adaptive dose-response analysis combines pre-specified dose-response modeling with planned interim looks that allow modifications — such as dropping ineffective doses or reallocating sample size — while maintaining statistical integrity. The most widely cited framework is MCP-Mod (Multiple Comparisons and Modeling), endorsed by the EMA and FDA as a fit-for-purpose methodology for dose-finding studies in drug development. | Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of disease. A demonstrated dose-response gradient is one of Bradford Hill's classic criteria supporting causal inference. |
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