Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Моделирование на основе анализа решений в экономике здравоохранения× | Модель Марковских цепей в экономике здравоохранения× | |
|---|---|---|
| Область | Экономика здравоохранения | Экономика здравоохранения |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1975 | 1983 |
| Автор метода≠ | Pauker & Kassirer (medical decision analysis, Massachusetts General Hospital) | Beck & Pauker (medical decision analysis, Massachusetts General Hospital) |
| Тип | Method | Method |
| Основополагающий источник≠ | Pauker, S. G., & Kassirer, J. P. (1975). Therapeutic Decision Making: A Cost-Benefit Analysis. New England Journal of Medicine, 293(5), 229-234. DOI ↗ | Beck, J. R., & Pauker, S. G. (1983). The Markov Process in Medical Prognosis. Medical Decision Making, 3(4), 419-458. DOI ↗ |
| Другие названия≠ | decision analysis, decision tree, decision model, health economic model | Markov model, state transition model, cohort simulation |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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