Vertaile menetelmiä

	Bayesian Dose-Response Analysis	Bayesiläinen verkko	Selviytymisanalyysi
Tieteenala	Epidemiologia	Bayesilainen tilastotiede	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Process / pipeline	Bayesian methods	Process / pipeline
Syntyvuosi	1990s–2000s (Bayesian formalization)	1988	1958
Kehittäjä	Developed from classical frequentist dose-response traditions; Bayesian formulations advanced by Dempster, Gelman, and colleagues	Judea Pearl	Edward L. Kaplan and Paul Meier
Tyyppi	Statistical modeling approach	Probabilistic graphical model	Method
Alkuperäislähde	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	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Rinnakkaisnimet	Bayesian DRA, Bayesian dose-response modeling, Bayesian benchmark dose analysis, BDR	Bayes network, belief network, probabilistic graphical model, directed graphical model	Kaplan-Meier analysis, Cox regression, TTE analysis
Liittyvät	3	4	3
Tiivistelmä	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.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.	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.