Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods
Kunst N, Wilson ECF, Glynn D, Alarid-Escudero F, Baio G, Brennan A, Fairley M, Goldhaber-Fiebert JD, Jackson C, Jalal H, Menzies NA, Strong M, Thom H, Heath A, Value of Information C. Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods. Value In Health 2020, 23: 734-742. PMID: 32540231, PMCID: PMC8183576, DOI: 10.1016/j.jval.2020.02.010.Peer-Reviewed Original ResearchCalculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies
Heath A, Kunst N, Jackson C, Strong M, Alarid-Escudero F, Goldhaber-Fiebert JD, Baio G, Menzies NA, Jalal H. Calculating the Expected Value of Sample Information in Practice: Considerations from 3 Case Studies. Medical Decision Making 2020, 40: 314-326. PMID: 32297840, PMCID: PMC7968749, DOI: 10.1177/0272989x20912402.Peer-Reviewed Original ResearchConceptsHealth economic modelApproximation methodEconomic modelTraditional Monte Carlo methodTraditional Monte CarloMonte Carlo algorithmMonte Carlo methodHigh computational burdenSample informationCarlo algorithmData generation processCarlo methodMonte CarloComputational burdenComputational speedPolicy decisionsComplex decision modelsComputation methodDecision modelRealistic exampleSample sizeEVSIDifferent examplesExpected valuesCarlo