Сравнение методов

	Многоцентровой анализ зависимости «доза-эффект»	Анализ «доза-эффект»	Когортное исследование с участием нескольких центров
Область	Эпидемиология	Эпидемиология	Эпидемиология
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Год появления	1992 (foundational trend methods); refined 2000s–2010s	Conceptual roots 16th century; modern epidemiological application mid-20th century	Mid-to-late 20th century (widespread adoption 1970s–1990s)
Автор метода	Greenland & Longnecker; extended by Orsini et al.	Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill	Developed incrementally through large collaborative epidemiological projects (e.g., Framingham Heart Study consortium expansions, 1948 onward; EPIC study, 1992)
Тип	Quantitative epidemiological analysis	Quantitative analytical method	Observational longitudinal study
Основополагающий источник	Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301-1309.	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
Другие названия	pooled dose-response analysis, multicenter exposure-response analysis, multi-site dose-response modeling, collaborative dose-response study	exposure-response analysis, concentration-response modeling, dose-response modeling, DRA	multisite cohort study, multi-centre cohort, collaborative cohort study, pooled cohort study
Связанные	2	4	6
Сводка	Multicenter dose-response analysis estimates the quantitative shape of the relationship between a graded exposure and a health outcome by pooling data or effect estimates across two or more study centers. Using flexible regression tools such as restricted cubic splines or fractional polynomials within a two-stage meta-analytic framework, it characterizes whether the relationship is linear, supra-linear, threshold-based, or J-shaped — providing far greater statistical power and generalizability than any single center could achieve alone.	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.	A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and population diversity that single-site designs cannot match, making it the workhorse of large-scale epidemiological and clinical research.