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When Is a Model Good Enough? Deriving the Expected Value of Model Improvement via Specifying Internal Model Discrepancies

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References

2005Methods for the Economic Evaluation of Health Care Programmes13,647 cited
2007Global Sensitivity Analysis. The Primer6,425 cited
2006Decision Modelling For Health Economic Evaluation3,144 cited
1969Decision Analysis: Introductory Lectures on Choices Under Uncertainty.1,928 cited
2006Methods for the economic evaluation of health care programmes, 3rd ed956 cited
2004Combining Field Data and Computer Simulations for Calibration and Prediction622 cited

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2019

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