Порівняння методів

	Байєсівський аналіз залежності «доза-відповідь»	Аналіз виживаності
Галузь	Епідеміологія	Статистика досліджень
Родина	Process / pipeline	Process / pipeline
Рік появи	1990s–2000s (Bayesian formalization)	1958
Автор методу	Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues	Edward L. Kaplan and Paul Meier
Тип	Statistical modeling approach	Method
Основоположне джерело	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	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Інші назви	Bayesian DRA, Bayesian dose-response modeling, Bayesian benchmark dose analysis, BDR	Kaplan-Meier analysis, Cox regression, TTE analysis
Пов'язані	3	3
Підсумок	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.	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.