Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.
Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.
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DOI：
10.1177/0272989x21995805
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发表时间：
2021-05
影响因子：
3.6
通讯作者：
Peterson, Josh
中科院分区：
文献类型：
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作者：
Graves, John;Garbett, Shawn;Zhou, Zilu;Schildcrout, Jonathan S.;Peterson, Josh
我们讨论了与健康经济决策建模方法相关的权衡和错误。通过在药物基因组学（PGx）测试中指导遗传变异个体的药物选择，我们评估了四种方法建模的相同决策场景的模型准确性、最优决策和计算时间：使用(1)耦合时间微分方程（DEQ）；(2)基于队列的离散时间状态转移模型[MARKOV]；(3)单个离散时间状态转换微仿真模型[MICROSIM]；(4)离散事件模拟[DES]。相对于DEQ， PGx测试的净货币效益（相对于不测试的参考策略）基于马尔可夫，使用常用公式进行率-概率转换，产生不同的最优决策。当使用转移强度矩阵嵌入转移概率时，马尔可夫几乎与DEQ相同。在随机模型中，DES模型输出收敛于DEQ，模拟患者数量（100万）大大少于MICROSIM（10亿）。总体而言，适当嵌入的马尔可夫模型提供了最有利的准确性和运行时间组合，但由于在适当嵌入转移概率后包含“跳跃”状态，因此为计算成本和质量调整寿命年结果引入了额外的复杂性。在随机模型中，DES提供了最有利的准确性、可靠性和速度组合。
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 four 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 (vs. 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) vs. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and run-time, 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.