  Background: The Expected Value of Sample Information (EVSI) calculates the
value of collecting additional information through a study with a given design.
Standard EVSI analyses assume that the treatment recommendations based on the
new information will be implemented immediately and completely once the study
has finished. However, treatment implementation is often slow and incomplete,
giving a biased estimation of the study value. Previous methods have adjusted
for this bias, but they typically make the unrealistic assumption that the
study outcomes do not impact the implementation. One method does assume that
the implementation is related to the strength of evidence in favour of the
treatment but this method uses analytical results, which require alternative
restrictive assumptions. Methods: We develop two implementation-adjusted EVSI
calculation methods that relax these assumptions. The first method uses
computationally demanding nested simulations, using the definition of the
implementation-adjusted EVSI. The second method aims to facilitate the
computation by adapting a recently developed efficient EVSI computation method
to adjust for imperfect implementation. The implementation-adjusted EVSI is
then calculated with the two methods across three examples. Results: The
maximum difference between the two 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: The methods developed in this paper calculate
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, we allow accurate
adjustments for imperfect implementation with the same computational cost as a
standard analysis.
