Open AccessComparison of Decision Modeling Approaches for Health Technology and Policy Evaluation
Author(s) -
John A. Graves,
Shawn Garbett,
Zilu Zhou,
Jonathan S. Schildcrout,
Josh F. Peterson
Publication year - 2021
Publication title -
medical decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 103
eISSN - 1552-681X
pISSN - 0272-989X
DOI - 10.1177/0272989x21995805
Subject(s) - markov chain , markov model , computer science , stochastic matrix , reliability (semiconductor) , mathematical optimization , markov decision process , decision model , econometrics , markov process , statistics , mathematics , machine learning , power (physics) , physics , quantum mechanics
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.