Department of Statistical Science, University College London, London, England, UK (AH, IM, GB).
Med Decis Making. 2018 Feb;38(2):163-173. doi: 10.1177/0272989X17738515. Epub 2017 Nov 10.
The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. Although this value would be important to both researchers and funders, there are very few practical applications of the EVSI. This is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations.
We present an approximation method for the EVSI that is framed in a Bayesian setting and is based on estimating the distribution of the posterior mean of the incremental net benefit across all possible future samples, known as the distribution of the preposterior mean. Specifically, this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluations.
This novel approximation method is applied to a health economic model that has previously been used to assess the performance of other EVSI estimators and accurately estimates the EVSI. The computational time for this method is competitive with other methods.
We have developed a new calculation method for the EVSI which is computationally efficient and accurate.
This novel method relies on some additional simulation so can be expensive in models with a large computational cost.
预期样本信息价值（EVSI）用于计算新研究策略的经济价值。尽管该价值对研究人员和资助者都很重要，但 EVSI 的实际应用却非常少。这是由于在使用嵌套模拟的实际健康经济模型中计算 EVSI 时存在计算上的困难。
我们提出了一种 EVSI 的近似方法，该方法基于贝叶斯框架，基于估计增量净效益后验均值在所有可能未来样本中的分布，即后验均值分布。具体来说，该分布是通过矩匹配和模拟来估计的，模拟可用于概率敏感性分析，这在健康经济评估中通常是强制性的。
该新的近似方法应用于先前用于评估其他 EVSI 估计量性能的健康经济模型，并准确估计了 EVSI。该方法的计算时间与其他方法具有竞争力。
我们已经开发了一种新的 EVSI 计算方法，该方法计算效率高且准确。
该新方法依赖于一些额外的模拟，因此在计算成本较高的模型中可能会很昂贵。
