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| 용량-반응 실험 설계 및 분석× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 실험설계 | 연구 통계 |
| 계열≠ | Hypothesis test | Process / pipeline |
| 기원 연도≠ | 1994 | 1958 |
| 창시자≠ | Classical pharmacology; formalized by ICH E4 (1994) and Ritz et al. (2015) | David Roxbee Cox |
| 유형≠ | Nonlinear curve fitting and monotone contrast testing | Method |
| 원전≠ | Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLOS ONE, 10(12), e0146021. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | dose-response analysis, dose-response curve, Doz-Yanıt Tasarımı ve Analizi (Dose-Response), ED50 analysis | logit model, binomial logistic regression, LR |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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