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| Projektowanie i analiza eksperymentów zależności dawka-odpowiedź× | Pełny czynnikowy plan eksperymentu× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Hypothesis test | Hypothesis test |
| Rok powstania≠ | 1994 | 1926 |
| Twórca≠ | Classical pharmacology; formalized by ICH E4 (1994) and Ritz et al. (2015) | R. A. Fisher |
| Typ≠ | Nonlinear curve fitting and monotone contrast testing | Parametric factorial experiment |
| Źródło pierwotne≠ | Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLOS ONE, 10(12), e0146021. DOI ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Inne nazwy≠ | dose-response analysis, dose-response curve, Doz-Yanıt Tasarımı ve Analizi (Dose-Response), ED50 analysis | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | Dose-response design is a framework for planning and analysing experiments that characterise the relationship between the amount of a stimulus — such as a drug dose or a chemical concentration — and the magnitude of a biological or physiological response. Formalised in regulatory guidance by the ICH E4 guideline (1994) and extensively developed in the statistical literature by Ritz et al. (2015), the framework covers experiment design, four-parameter and five-parameter logistic curve fitting, key benchmark estimates (ED50/EC50, NOAEL, LOAEL), and monotone trend testing via the Williams procedure. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
| ScholarGateZbiór danych ↗ |
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