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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Strategies for efficient computation of the expected value of partial perfect information
Medical Decision Making, Volume 34, No. 3, Year 2014
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Description
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. © The Author(s) 2014.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC4948652/bin/DS_10.11770272989X13514774_Appendix.pdf
https://efashare.b-cdn.net/share/pmc/articles/PMC4948652/bin/DS_10.11770272989X13514774_Table_A1.pdf
Authors & Co-Authors
Madan, Jason J.
United Kingdom, Bristol
University of Bristol
United Kingdom, Coventry
Warwick Medical School
Ades, Anthony E.
United Kingdom, Bristol
University of Bristol
Price, Malcolm J.
United Kingdom, Bristol
University of Bristol
United Kingdom, Birmingham
University of Birmingham
Maitland, Kathryn M.
United Kingdom, London
Imperial College London
Kenya, Nairobi
Wellcome Trust Research Laboratories Nairobi
Jemutai, Julie
Kenya, Nairobi
Wellcome Trust Research Laboratories Nairobi
Revill, Paul A.
United Kingdom, York
University of York
Welton, Nicky J.
United Kingdom, Bristol
University of Bristol
Statistics
Citations: 34
Authors: 7
Affiliations: 6
Identifiers
Doi:
10.1177/0272989X13514774
ISSN:
0272989X
e-ISSN:
1552681X
Research Areas
Infectious Diseases
Maternal And Child Health