Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

John Graves, Shawn Garbett, Zilu Zhou, Jonathan S. Schildcrout and Josh Peterson
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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

Medical Decision Making, 2021, vol. 41, issue 4, 453-464

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.

Keywords: decision modeling; pharmacogenomics; health economic methods (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:41:y:2021:i:4:p:453-464

DOI: 10.1177/0272989X21995805

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