Сравнение на методи

	Метод на инструменталните променливи (IV) за причинно-следствен анализ	Модел на Марковска верига в здравната икономика
Област	Икономика на здравеопазването	Икономика на здравеопазването
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1990s (modern applications)	1983
Създател	Angrist & Pischke (applied econometrics); rooted in econometric theory	Beck & Pauker (medical decision analysis, Massachusetts General Hospital)
Тип	Method	Method
Основополагащ източник	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458.
Други названия	IV, two-stage least squares, TSLS, causal estimation	Markov model, state transition model, cohort simulation
Свързани	3	5
Резюме	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.	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.