微观模拟对决策不确定性和信息价值的估计有偏差但一致

Jeremy D. Goldhaber-Fiebert, Hawre Jalal, Fernando Alarid Escudero
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引用次数: 0

摘要

个体水平的状态转换微观模拟(iSTMs)在经济评估中大量出现,以取代队列状态转换模型(cSTMs)。先前的研究表明,iSTM 可提供无偏的预期增量净货币效益(EINMB)估算值,但其对决策不确定性和信息价值(VOI)估算值的统计特性尚未定性。我们将 iSTM 得出的估计值与相应的 cSTM 进行比较。对于 2 备选决策和正态分布的增量成本和收益,我们推导出了 cSTM 和 iSTM 的成本效益概率和完美信息预期值 (EVPI) 的分析表达式,并考虑了人群和个体层面增量结果的相关性。iSTM 对决策不确定性和 VOI 的估计是有偏差的,但在渐近上是一致的(即当微观模拟个体数大于无限时,偏差大于 0)。决策不确定性取决于 INMB 分布的一个尾部(如 P(INMB<=0)),而 INMB 分布的尾部取决于估计方差(考虑到一阶噪声,iSTM 的估计方差更大)。虽然 iSTM 高估了 EVPI,但其对具有成本效益概率的偏差方向并不明确。当增量成本和效果的不确定性呈负相关时,偏差会更大。虽然在总体不确定性水平上更多的样本可以与更多的微观模拟互换,以估计 EINMB,但在估计决策不确定性和 VOI 时尽量减少 iSTM 偏差取决于足够的微观模拟。分析人员在分配计算预算时应考虑到这一点,并至少在报告结果中描述这种偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent
Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Prior studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of their estimates of decision uncertainty and VOI are uncharacterized. We compare such iSTMs-produced estimates to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. Numerical simulations illustrate our findings and explore relaxation of normality assumptions or having >2 decision alternatives. iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias->0 as number of microsimulated individuals->infinity). Decision uncertainty depends on one tail of the INMB distribution (e.g., P(INMB<=0)) which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results.
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