Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

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.

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“…One drawback of the microsimulation approach in general is its use of discrete time to approximate an underlying continuous process. This can be problematic for several reasons, as discussed by Graves et al 43 They recommend using discrete event simulation models with continuous time as an alternative. Adapting our framework to a DES model is an important area for future research.…”

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“…and are therefore a better design for screening type interventions. 54 However, they do require more extensive individual patient data and are also computationally more intensive. Finally, decision trees are logic-based designs lacking the ability to account for time dependent variables or recurring events and are therefore the simpler of the three modelling techniques.…”