  Objective: The Expected Value of Sample Information (EVSI) quantifies the
economic benefit of reducing uncertainty in a health economic model by
collecting additional information. This has the potential to improve the
allocation of research budgets. Despite this, practical EVSI evaluations are
limited, partly due to the computational cost of estimating this value using
the "gold-standard" nested simulation methods. Recently, however, Heath et al
developed an estimation procedure that reduces the number of simulations
required for this "gold-standard" calculation. Up to this point, this new
method has been presented in purely technical terms. Study Design: This study
presents the practical application of this new method to aid its
implementation. We use a worked example to illustrate the key steps of the EVSI
estimation procedure before discussing its optimal implementation using a
practical health economic model. Methods: The worked example is based on a
three parameter linear health economic model. The more realistic model
evaluates the cost-effectiveness of a new chemotherapy treatment which aims to
reduce the number of side effects experienced by patients. We use a Markov
Model structure to evaluate the health economic profile of experiencing side
effects. Results: This EVSI estimation method offers accurate estimation within
a feasible computation time, seconds compared to days, even for more complex
model structures. The EVSI estimation is more accurate if a greater number of
nested samples are used, even for a fixed computational cost. Conclusions: This
new method reduces the computational cost of estimating the EVSI by nested
simulation.
