Department of Statistical Science, University College London, London, UK.
Department of Statistical Science, University College London, London, UK.
Value Health. 2018 Nov;21(11):1299-1304. doi: 10.1016/j.jval.2018.05.004. Epub 2018 Jul 17.
The expected value of sample information (EVSI) quantifies the economic benefit of reducing uncertainty in a health economic model by collecting additional information. This has the potential to improve the allocation of research budgets. Despite this, practical EVSI evaluations are limited partly due to the computational cost of estimating this value using the gold-standard nested simulation methods. Recently, however, Heath et al. developed an estimation procedure that reduces the number of simulations required for this gold-standard calculation. Up to this point, this new method has been presented in purely technical terms.
This study presents the practical application of this new method to aid its implementation. We use a worked example to illustrate the key steps of the EVSI estimation procedure before discussing its optimal implementation using a practical health economic model.
The worked example is based on a three-parameter linear health economic model. The more realistic model evaluates the cost-effectiveness of a new chemotherapy treatment, which aims to reduce the number of side effects experienced by patients. We use a Markov model structure to evaluate the health economic profile of experiencing side effects.
This EVSI estimation method offers accurate estimation within a feasible computation time, seconds compared to days, even for more complex model structures. The EVSI estimation is more accurate if a greater number of nested samples are used, even for a fixed computational cost.
This new method reduces the computational cost of estimating the EVSI by nested simulation.
样本信息的预期价值（EVSI）通过收集额外信息来量化减少健康经济模型不确定性的经济收益。这有可能改善研究预算的分配。尽管如此，由于使用黄金标准嵌套模拟方法估计此值的计算成本，实际的 EVSI 评估受到限制。然而，最近 Heath 等人开发了一种估算程序，该程序减少了进行这种黄金标准计算所需的模拟次数。到目前为止，这种新方法仅以纯技术术语呈现。
本研究介绍了这种新方法的实际应用，以帮助其实施。我们使用一个实例来说明 EVSI 估算程序的关键步骤，然后讨论如何使用实际的健康经济模型来优化其实施。
该实例基于三参数线性健康经济模型。更现实的模型评估了一种新的化疗治疗的成本效益，该治疗旨在减少患者经历的副作用数量。我们使用马尔可夫模型结构来评估经历副作用的健康经济状况。
这种 EVSI 估算方法在可行的计算时间内提供了准确的估计，与几天相比，仅需几秒钟，即使对于更复杂的模型结构也是如此。如果使用更多的嵌套样本，即使在固定的计算成本下，EVSI 估算也会更加准确。
这种新方法通过嵌套模拟降低了估计 EVSI 的计算成本。
