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Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

Author

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  • John Graves

    (Department of Health Policy, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA)

  • Shawn Garbett

    (Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA)

  • Zilu Zhou

    (Department of Health Policy, Vanderbilt University Medical Center, Nashville, TN, USA)

  • Jonathan S. Schildcrout

    (Department of Biostatistics, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA)

  • Josh Peterson

    (Department of Biomedical Informatics, Vanderbilt University School of Medicine Vanderbilt University Medical Center, Nashville, TN, USA)

Abstract

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of “jumpover†states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

Suggested Citation

  • John Graves & Shawn Garbett & Zilu Zhou & Jonathan S. Schildcrout & Josh Peterson, 2021. "Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation," Medical Decision Making, , vol. 41(4), pages 453-464, May.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:4:p:453-464
    DOI: 10.1177/0272989X21995805
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    References listed on IDEAS

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    1. P. Thokala & A. Duenas, 2012. "Multiple Criteria Decision Analysis for Health Technology Assessment," Post-Print hal-00800398, HAL.
    2. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
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    5. Peacock, Stuart & Mitton, Craig & Bate, Angela & McCoy, Bonnie & Donaldson, Cam, 2009. "Overcoming barriers to priority setting using interdisciplinary methods," Health Policy, Elsevier, vol. 92(2-3), pages 124-132, October.
    6. Sheldon H. Jacobson & Shane N. Hall & James R. Swisher, 2006. "Discrete-Event Simulation of Health Care Systems," International Series in Operations Research & Management Science, in: Randolph W. Hall (ed.), Patient Flow: Reducing Delay in Healthcare Delivery, chapter 0, pages 211-252, Springer.
    7. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
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    Cited by:

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