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Background: The Expected Value of Sample Information (EVSI) calculates thevalue of collecting additional information through a study with a given design.Standard EVSI analyses assume that the treatment recommendations based on thenew information will be implemented immediately and completely once the studyhas finished. However, treatment implementation is often slow and incomplete,giving a biased estimation of the study value. Previous methods have adjustedfor this bias, but they typically make the unrealistic assumption that thestudy outcomes do not impact the implementation. One method does assume thatthe implementation is related to the strength of evidence in favour of thetreatment but this method uses analytical results, which require alternativerestrictive assumptions. Methods: We develop two implementation-adjusted EVSIcalculation methods that relax these assumptions. The first method usescomputationally demanding nested simulations, using the definition of theimplementation-adjusted EVSI. The second method aims to facilitate thecomputation by adapting a recently developed efficient EVSI computation methodto adjust for imperfect implementation. The implementation-adjusted EVSI isthen calculated with the two methods across three examples. Results: Themaximum difference between the two methods is at most 6% in all examples. Theefficient computation method is between 6 and 60 times faster than the nestedsimulation method in this case study and could be used in practice.Conclusions: The methods developed in this paper calculateimplementation-adjusted EVSI using realistic assumptions. The efficientestimation method is accurate and can estimate the implementation-adjusted EVSIin practice. By adapting standard EVSI estimation methods, we allow accurateadjustments for imperfect implementation with the same computational cost as astandard analysis.

Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation