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Influence of Modeling Choices on Value of Information Analysis: An Empirical Analysis from a Real-World Experiment

Author

Listed:
  • David D. Kim

    (Tufts Medical Center)

  • Gregory F. Guzauskas

    (University of Washington)

  • Caroline S. Bennette

    (Flatiron Health)

  • Anirban Basu

    (University of Washington)

  • David L. Veenstra

    (University of Washington)

  • Scott D. Ramsey

    (Fred Hutchinson Cancer Research Center)

  • Josh J. Carlson

    (University of Washington)

Abstract

Background Value of information (VOI) analysis often requires modeling to characterize and propagate uncertainty. In collaboration with a cancer clinical trial group, we integrated a VOI approach to assessing trial proposals. Objective This paper aims to explore the impact of modeling choices on VOI results and to share lessons learned from the experience. Methods After selecting two proposals (A: phase III, breast cancer; B: phase II, pancreatic cancer) for in-depth evaluations, we categorized key modeling choices relevant to trial decision makers (characterizing uncertainty of efficacy, evidence thresholds to change clinical practice, and sample size) and modelers (cycle length, survival distribution, simulation runs, and other choices). Using a $150,000 per quality-adjusted life-year (QALY) threshold, we calculated the patient-level expected value of sample information (EVSI) for each proposal and examined whether each modeling choice led to relative change of more than 10% from the averaged base-case estimate. We separately analyzed the impact of the effective time horizon. Results The base-case EVSI was $118,300 for Proposal A and $22,200 for Proposal B per patient. Characterizing uncertainty of efficacy was the most important choice in both proposals (e.g. Proposal A: $118,300 using historical data vs. $348,300 using expert survey), followed by the sample size and the choice of survival distribution. The assumed effective time horizon also had a substantial impact on the population-level EVSI. Conclusions Modeling choices can have a substantial impact on VOI. Therefore, it is important for groups working to incorporate VOI into research prioritization to adhere to best practices, be clear in their reporting and justification for modeling choices, and to work closely with the relevant decision makers, with particular attention to modeling choices.

Suggested Citation

  • David D. Kim & Gregory F. Guzauskas & Caroline S. Bennette & Anirban Basu & David L. Veenstra & Scott D. Ramsey & Josh J. Carlson, 2020. "Influence of Modeling Choices on Value of Information Analysis: An Empirical Analysis from a Real-World Experiment," PharmacoEconomics, Springer, vol. 38(2), pages 171-179, February.
  • Handle: RePEc:spr:pharme:v:38:y:2020:i:2:d:10.1007_s40273-019-00848-8
    DOI: 10.1007/s40273-019-00848-8
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    References listed on IDEAS

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    1. Linda Ryen & Mikael Svensson, 2015. "The Willingness to Pay for a Quality Adjusted Life Year: A Review of the Empirical Literature," Health Economics, John Wiley & Sons, Ltd., vol. 24(10), pages 1289-1301, October.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    3. Laura Bojke & Bogdan Grigore & Dina Jankovic & Jaime Peters & Marta Soares & Ken Stein, 2017. "Informing Reimbursement Decisions Using Cost-Effectiveness Modelling: A Guide to the Process of Generating Elicited Priors to Capture Model Uncertainties," PharmacoEconomics, Springer, vol. 35(9), pages 867-877, September.
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