Background The expected value of sample information (EVSI) calculates the value of collecting additional information through a research study with a given design. However, standard EVSI analyses do not account for the slow and often incomplete implementation of the treatment recommendations that follow research. Thus, standard EVSI analyses do not correctly capture the value of the study. Previous research has developed measures to calculate the research value while adjusting for implementation challenges, but estimating these measures is a challenge. Methods Based on a method that assumes the implementation level is related to the strength of evidence in favor of the treatment, 2 implementation-adjusted EVSI calculation methods are developed. These novel methods circumvent the need for analytical calculations, which were restricted to settings in which normality could be assumed. The first method developed in this article uses computationally demanding nested simulations, based on the definition of the implementation-adjusted EVSI. The second method is based on adapting the moment matching method, a recently developed efficient EVSI computation method, to adjust for imperfect implementation. The implementation-adjusted EVSI is then calculated with the 2 methods across 3 examples. Results The maximum difference between the 2 methods is at most 6% in all examples. The efficient computation method is between 6 and 60 times faster than the nested simulation method in this case study and could be used in practice. Conclusions This article permits the calculation of an implementation-adjusted EVSI using realistic assumptions. The efficient estimation method is accurate and can estimate the implementation-adjusted EVSI in practice. By adapting standard EVSI estimation methods, adjustments for imperfect implementation can be made with the same computational cost as a standard EVSI analysis. Highlights Standard expected value of sample information (EVSI) analyses do not account for the fact that treatment implementation following research is often slow and incomplete, meaning they incorrectly capture the value of the study. Two methods, based on nested Monte Carlo sampling and the moment matching EVSI calculation method, are developed to adjust EVSI calculations for imperfect implementation when the speed and level of the implementation of a new treatment depends on the strength of evidence in favor of the treatment. The 2 methods we develop provide similar estimates for the implementation-adjusted EVSI. Our methods extend current EVSI calculation algorithms and thus require limited additional computational complexity.
Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning
