Сравнение методов

	Анализ «доза-эффект»	Анализ выживаемости
Область	Эпидемиология	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	Conceptual roots 16th century; modern epidemiological application mid-20th century	1958
Автор метода	Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill	Edward L. Kaplan and Paul Meier
Тип	Quantitative analytical method	Method
Основополагающий источник	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Другие названия	exposure-response analysis, concentration-response modeling, dose-response modeling, DRA	Kaplan-Meier analysis, Cox regression, TTE analysis
Связанные	4	3
Сводка	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.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.