Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский анализ зависимости «доза-эффект»× | Логистическая регрессия× | |
|---|---|---|
| Область≠ | Эпидемиология | Статистика исследований |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1990s–2000s (Bayesian formalization) | 1958 |
| Автор метода≠ | Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues | David Roxbee Cox |
| Тип≠ | Statistical modeling approach | Method |
| Основополагающий источник≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Другие названия≠ | Bayesian DRA, Bayesian dose-response modeling, Bayesian benchmark dose analysis, BDR | logit model, binomial logistic regression, LR |
| Связанные | 3 | 3 |
| Сводка≠ | Bayesian dose-response analysis models the relationship between the level of exposure (dose) to a substance and the magnitude or probability of a biological response, embedding that model in a Bayesian probabilistic framework. Unlike frequentist approaches that yield a single point estimate with confidence intervals, the Bayesian framework produces a full posterior distribution over model parameters, allowing explicit quantification of uncertainty, incorporation of prior scientific knowledge, and principled model averaging. It is widely applied in toxicology, pharmacology, environmental risk assessment, and clinical dose-finding studies. | 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. |
| ScholarGateНабор данных ↗ |
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