In Value of Information (VoI) analysis, the unit normal loss integral (UNLI) frequently emerges as a solution for the computation of various VoI metrics. However, one limitation of the UNLI has been that its closed-form solution is available for only one dimension, and thus can be used for comparisons involving only two strategies (where it is applied to the scalar incremental net benefit). We derived a closed-form solution for the two-dimensional UNLI, enabling closed-form VoI calculations for three strategies. We verified the accuracy of this method via simulation studies. A case study based on a three-arm clinical trial was used as an example. VoI methods based on the closed-form solutions for the UNLI can now be extended to three-decision comparisons, taking a fraction of a second to compute and not being subject to Monte Carlo error. An R implementation of this method is provided as part of the predtools package (https://github.com/resplab/predtools/).
翻译:在信息值(VoI)分析中,单位正常损失整体部分(UNLI)经常出现,作为计算各种VoI指标的一种解决办法。然而,UNLI的一个局限性是,其封闭式解决办法只有一个维度,因此可用于比较仅涉及两个战略(在对卡路里增量净收益适用的情况下),我们为二维UNLI得出了一个封闭式解决办法,使三个战略的封闭式VoI计算成为可能。我们通过模拟研究核实了这一方法的准确性。我们使用了基于三重临床试验的案例研究作为例子。基于UNLI封闭式解决办法的VoI方法现在可以扩大到三个决定性比较,用一小部分的第二位进行计算,而不受Monte Carlo错误的影响。该方法的实施作为前工具包的一部分(https://github.com/resplab/predtools/)提供R的落实方法。