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| 비용효과 분석 (CEA)× | 의료 경제학에서의 마르코프 연쇄 모형× | |
|---|---|---|
| 분야 | 보건경제학 | 보건경제학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1984 | 1983 |
| 창시자≠ | Drummond & Stoddart (Health Economics Research Group, McMaster University) | Beck & Pauker (medical decision analysis, Massachusetts General Hospital) |
| 유형 | Method | Method |
| 원전≠ | 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. link ↗ | Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458. DOI ↗ |
| 별칭 | CEA, ICER, Incremental Cost-Effectiveness Ratio | Markov model, state transition model, cohort simulation |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
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