ArticleStatistics in medicine2016
Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A Pragmatic Bayesian Adaptive Trial Design Based on the Value of Information: The Value-Driven Adaptive Design.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Prioritizing Research in an Era of Personalized Medicine: The Potential Value of Unexplained Heterogeneity.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Value of Information Analysis in Models to Inform Health Policy.Annual review of statistics and its application · 2022Article
- Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- A data value metric for quantifying information content and utility.Journal of big data · 2021Article
- A guide to value of information methods for prioritising research in health impact modelling.Epidemiologic methods · 2021Article
- Computing the Expected Value of Sample Information Efficiently: Practical Guidance and Recommendations for Four Model-Based Methods.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2020Article
- Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2020Review
- Cost-effectiveness and value of information analysis of a low-friction environment following skin graft in patients with burn injury.Pilot and feasibility studies · 2020Article
- A Flexible Open-Source Decision Model for Value Assessment of Biologic Treatment for Rheumatoid Arthritis.PharmacoEconomics · 2019Article
- Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.Journal of the American Statistical Association · 2019Article
- A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease.BMC medical research methodology · 2018Article
- Article
- Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.Statistics in medicine · 2016Article
Corrections and comments
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3 authors.
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Abstract
The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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