Strategies for efficient computation of the expected value of partial perfect information.
Expected value of information methods evaluate the potential health benefits that can be obtained from conducting new research to reduce uncertainty in the parameters of a cost-effectiveness analysis model, hence reducing decision uncertainty. Expected value of partial perfect information (EVPPI) provides an upper limit to the health gains that can be obtained from conducting a new study on a subset of parameters in the cost-effectiveness analysis and can therefore be used as a sensitivity analysis to identify parameters that most contribute to decision uncertainty and to help guide decisions around which types of study are of most value to prioritize for funding. A common general approach is to use nested Monte Carlo simulation to obtain an estimate of EVPPI. This approach is computationally intensive, can lead to significant sampling bias if an inadequate number of inner samples are obtained, and incorrect results can be obtained if correlations between parameters are not dealt with appropriately. In this article, we set out a range of methods for estimating EVPPI that avoid the need for nested simulation: reparameterization of the net benefit function, Taylor series approximations, and restricted cubic spline estimation of conditional expectations. For each method, we set out the generalized functional form that net benefit must take for the method to be valid. By specifying this functional form, our methods are able to focus on components of the model in which approximation is required, avoiding the complexities involved in developing statistical approximations for the model as a whole. Our methods also allow for any correlations that might exist between model parameters. We illustrate the methods using an example of fluid resuscitation in African children with severe malaria.
School of Social and Community Medicine, University of Bristol, Bristol, UK (JM, AEA, MP, NJW).|Warwick Medical School, University of Warwick, Coventry, UK (JM)
Jason
J
Madan
School of Social and Community Medicine, University of Bristol, Bristol, UK (JM, AEA, MP, NJW).
Anthony E
AE
Ades
School of Social and Community Medicine, University of Bristol, Bristol, UK (JM, AEA, MP, NJW).|School of Health and Population Sciences, University of Birmingham, Birmingham, UK (MP)
Malcolm
M
Price
Department of Medicine, Imperial College, London, UK (KM)|KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya (KM, JJ)
Kathryn
K
Maitland
KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya (KM, JJ)
Julie
J
Jemutai
Centre for Health Economics, University of York, York, UK (PR)
Paul
P
Revill
School of Social and Community Medicine, University of Bristol, Bristol, UK (JM, AEA, MP, NJW).
Nicky J
NJ
Welton
D019295:Computational Biology; D003657:Decision Making; D006292:Health Priorities; D009010:Monte Carlo Method; D035501:Uncertainty
Bayesian methods; cost-effectiveness analysis; value-of-information