Goldhaber-Fiebert Jeremy D, Jalal Hawre, Alarid-Escudero Fernando
Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA.
Center for Health Policy, Freeman Spogli Institute, Stanford University, Stanford, CA, USA.
Med Decis Making. 2025 Feb;45(2):127-142. doi: 10.1177/0272989X241305414. Epub 2024 Dec 25.
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). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
We compare iSTM-produced estimates of decision uncertainty and VOI 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. We use numerical simulations to illustrate our findings and explore the impact of relaxing normality assumptions or having >2 decision alternatives.
iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias approaches 0 as number of microsimulated individuals approaches infinity). Decision uncertainty depends on 1 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 since this increases INMB variance.
iSTMs are useful for probabilistic economic evaluations. 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.
Individual-level state-transition microsimulation models (iSTMs) produce biased but consistent estimates of the probability that interventions are cost-effective.iSTMs also produce biased but consistent estimates of the expected value of perfect information.The biases in these decision uncertainty and value-of-information measures are not reduced by more parameter sets being sampled from their population-level uncertainty distribution but rather by more individuals being microsimulated for each parameter set sampled.Analysts using iSTMs to quantify decision uncertainty and value of information should account for these biases when allocating their computational budgets and, at minimum, characterize such bias in their reported results.
个体水平状态转换微观模拟（iSTM）已在经济评估中大量涌现，以取代队列状态转换模型（cSTM）。概率性经济评估量化决策不确定性和信息价值（VOI）。先前的研究表明，iSTM能提供无偏的预期增量净货币效益（EINMB）估计值，但iSTM产生的决策不确定性和VOI估计值的统计特性仍未得到描述。
我们将iSTM产生的决策不确定性和VOI估计值与相应的cSTM进行比较。对于一个二选一的决策以及正态分布的增量成本和效益，我们推导了cSTM和iSTM在人群和个体水平上增量结果存在相关性时的成本效益概率和完美信息期望值（EVPI）的解析表达式。我们使用数值模拟来说明我们的发现，并探讨放宽正态性假设或有超过两个决策选项的影响。
iSTM对决策不确定性和VOI的估计存在偏差，但渐近一致（即随着微观模拟个体数量趋近于无穷大，偏差趋近于0）。决策不确定性取决于增量净货币效益（INMB）分布的一个尾部（例如，P[INMB <0]），这取决于估计的方差（由于一阶噪声，iSTM的方差更大）。虽然iSTM高估了EVPI，但其成本效益概率的偏差方向不明确。当增量成本和效果的不确定性呈负相关时，偏差会更大，因为这会增加INMB的方差。
iSTM对概率性经济评估很有用。虽然在人群不确定性水平上更多的样本与更多的微观模拟在估计EINMB方面是可互换的，但在估计决策不确定性和VOI时将iSTM偏差最小化取决于足够的微观模拟。分析师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。
个体水平状态转换微观模拟模型（iSTM）对干预措施具有成本效益的概率产生有偏差但一致的估计。iSTM对完美信息期望值也产生有偏差但一致的估计。这些决策不确定性和信息价值度量中的偏差不会因从人群水平不确定性分布中抽取更多参数集而减少，而是因对每个抽取的参数集进行更多个体的微观模拟而减少。使用iSTM来量化决策不确定性和信息价值的分析师在分配计算预算时应考虑这些偏差，并至少在报告结果中描述这种偏差。
