Heath, A;
Manolopoulou, I;
Baio, G;
(2016)
Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.
Statistics in Medicine
, 35
(23)
pp. 4264-4280.
10.1002/sim.6983.
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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.
| Type: | Article |
|---|---|
| Title: | Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/sim.6983 |
| Publisher version: | http://dx.doi.org/10.1002/sim.6983 |
| Language: | English |
| Additional information: | Copyright © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Mathematical & Computational Biology, Public, Environmental & Occupational Health, Medical Informatics, Medicine, Research & Experimental, Statistics & Probability, Research & Experimental Medicine, Mathematics, SPDE-INLA, Gaussian Process regression, Health economic evaluation, Value of information, Cost-effectiveness Analysis, Probabilistic Sensitivity-analysis, Clinical-trial Data, Dimension Reduction, Technology-assessment, Bayesian-approach, Gaussian Models, Regression, Uncertainty, Framework |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1467157 |
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