Σύγκριση μεθόδων

	Ανάλυση Προοπτικής Δοσο-Απόκρισης	Ανάλυση Επιβίωσης
Πεδίο	Επιδημιολογία	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1965 (Hill's criteria); widely applied through 1980s–present	1958
Δημιουργός	Bradford Hill (causal criteria including dose-response, 1965); formalized in modern epidemiology by Rothman, Greenland and others	Edward L. Kaplan and Paul Meier
Τύπος	Analytical epidemiological study design	Method
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	prospective exposure-response analysis, prospective trend analysis, forward-looking dose-response study, prospective gradient analysis	Kaplan-Meier analysis, Cox regression, TTE analysis
Συναφείς	4	3
Σύνοψη	Prospective dose-response analysis is an epidemiological approach that measures exposure levels in a defined population before outcomes occur, then quantifies how the risk or magnitude of an outcome changes systematically as exposure increases. By collecting exposure data prospectively, researchers can establish temporal sequence, reduce recall bias, and assess whether a biological gradient — one of Hill's classic causal criteria — exists between the agent of interest and a health outcome.	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.