Сравнение методов

	Анализ зависимости «доза-эффект» с поправкой на риск	Логистическая регрессия
Область	Эпидемиология	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1980s-1990s (formalized in modern epidemiology)	1958
Автор метода	Sander Greenland; Kenneth Rothman (foundational epidemiological methods)	David Roxbee Cox
Тип	Epidemiological modeling technique	Method
Основополагающий источник	Greenland, S. (1995). Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology, 6(4), 356-365.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Другие названия	confounder-adjusted dose-response, covariate-adjusted dose-response modeling, risk-stratified dose-response analysis, adjusted exposure-response analysis	logit model, binomial logistic regression, LR
Связанные	4	3
Сводка	Risk-adjusted dose-response analysis quantifies the relationship between increasing levels of an exposure (dose) and the probability or magnitude of an outcome (response), while simultaneously controlling for baseline risk factors that could confound or modify this relationship. The method is widely applied in clinical epidemiology, pharmacoepidemiology, and environmental health research to isolate the causal contribution of exposure intensity from background risk heterogeneity among participants.	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.