Σύγκριση μεθόδων

	Μοντελοποίηση Αναλυτικής Λήψης Αποφάσεων στην Οικονομική της Υγείας	Μοντέλο Αλυσίδας Markov στην Οικονομική της Υγείας
Πεδίο	Οικονομικά της Υγείας	Οικονομικά της Υγείας
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1975	1983
Δημιουργός	Pauker & Kassirer (medical decision analysis, Massachusetts General Hospital)	Beck & Pauker (medical decision analysis, Massachusetts General Hospital)
Τύπος	Method	Method
Θεμελιώδης πηγή	Pauker, S. G., & Kassirer, J. P. (1975). Therapeutic Decision Making: A Cost-Benefit Analysis. New England Journal of Medicine, 293(5), 229-234.	Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.
Εναλλακτικές ονομασίες	decision analysis, decision tree, decision model, health economic model	Markov model, state transition model, cohort simulation
Συναφείς	5	5
Σύνοψη	Decision analytic modeling is a systematic framework for comparing health interventions by integrating evidence on probabilities, outcomes, costs, and patient preferences into a quantitative model. Developed by Pauker and Kassirer in 1975, decision analysis structures clinical uncertainty and economic trade-offs, enabling transparent comparison of treatment options and identification of optimal strategies. Used in health technology assessment, clinical practice guideline development, and resource allocation decisions.	A Markov model is a decision-analytic tool that simulates disease progression through defined health states over time, calculating cumulative costs and quality-adjusted life years (QALYs) to enable cost-effectiveness analysis. Developed by Beck and Pauker in 1983, Markov models are now the standard framework for projecting long-term outcomes of health interventions, especially chronic diseases where patients transition between clinical states (treatment response, disease progression, remission, death). Used by health technology assessment bodies and pharmaceutical companies to predict intervention value beyond trial duration.