Sammenlign metoder

	Markov-model i sundhedsøkonomi	Omkostningseffektivitetsanalyse (CEA)
Fagområde	Sundhedsøkonomi	Sundhedsøkonomi
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1983	1984
Ophavsperson	Beck & Pauker (medical decision analysis, Massachusetts General Hospital)	Drummond & Stoddart (Health Economics Research Group, McMaster University)
Type	Method	Method
Oprindelig kilde	Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.	Gold, M. R., Siegel, J. E., Russell, L. B., & Weinstein, M. C. (Eds.). (1996). Cost-Effectiveness in Health and Medicine. New York: Oxford University Press.
Aliasser	Markov model, state transition model, cohort simulation	CEA, ICER, Incremental Cost-Effectiveness Ratio
Relaterede	5	5
Resumé	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.	Cost-effectiveness analysis compares the incremental cost per unit of health benefit gained by one intervention relative to a comparator (standard care or best alternative). Developed rigorously in the 1980s by Drummond, Stoddart, and colleagues, CEA is now the standard framework for technology appraisal globally. NICE, HAS, CADTH, and other health technology assessment bodies use CEA to decide which treatments warrant public funding and at what price.