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| Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả× | Mô hình chuỗi Markov trong kinh tế y tế× | |
|---|---|---|
| Lĩnh vực | Kinh tế học y tế | Kinh tế học y tế |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1990s (modern applications) | 1983 |
| Người khởi xướng≠ | Angrist & Pischke (applied econometrics); rooted in econometric theory | Beck & Pauker (medical decision analysis, Massachusetts General Hospital) |
| Loại | Method | Method |
| Công trình gốc≠ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ | Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458. DOI ↗ |
| Tên gọi khác≠ | IV, two-stage least squares, TSLS, causal estimation | Markov model, state transition model, cohort simulation |
| Liên quan≠ | 3 | 5 |
| Tóm tắt≠ | 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. |
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