Sage Journals: Discover world-class research

Abstract

In recent years, value-of-information analysis has become more widespread in health economic evaluations, specifically as a tool to guide further research and perform probabilistic sensitivity analysis. This is partly due to methodological advancements allowing for the fast computation of a typical summary known as the expected value of partial perfect information (EVPPI). A recent review discussed some approximation methods for calculating the EVPPI, but as the research has been active over the intervening years, that review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present two case studies in order to compare the estimation performance of these new methods. We conclude that a method based on nonparametric regression offers the best method for calculating the EVPPI in terms of accuracy, computational time, and ease of implementation. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with R functions and a web app to aid practitioners.

Keywords

probabilistic sensitivity analysis value of information computation methods EVPPI

Get full access to this article

View all access options for this article.

References

Claxton

Sculpher

McCabe

Briggs

Akehurst

Buxton

Brazier

O’Hagan

. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ. 2005;14:339–47.

Methods for health economic evaluations: a guideline based on current practices in Europe (2nd draft). Diemen (The Netherlands): European Network for Health Technology Assessment;2014.

Guidelines for the economic evaluation of health technologies: Canada (3rd ed). Ottawa (ON): Canadian Agency for Drugs and Technologies in Health; 2006.

Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee: version 4.3. Canberra (Australia): Department of Health and Ageing; 2008.

Howard

. Information value theory. IEEE Transactions on Systems Science and Cybernetics. 1966;2(1):22-26.

Ades

Claxton

. Expected value of sample information calculations in medical decision modeling. Med Decis Making. 2004;24:207–27.

Brennan

Kharroubi

. Efficient computation of partial expected value of sample information using Bayesian approximation. Research Report 560/05, Department of Probability and Statistics, University of Sheffield, UK;2005.

Briggs

Sculpher

Claxton

. Decision modelling for health economic evaluation. Oxford, UK: Oxford University Press; 2006.

Claxton

. Bayesian approaches to the value of information: implications for the regulation of new pharmaceutical. Health Econ. 1999;8:269–74.

10.

Claxton

Neumann

Araki

Weinstein

. Bayesian value-of-information analysis. Int J Technol Assess Health Care. 2001;17:38–55.

11.

Felli

Hazen

. Sensitivity analysis and the expected value of perfect information. Med Decis Making. 1998;18:95–109.

12.

Felli

Hazen

. A Bayesian approach to sensitivity analysis. Health Econ. 1999;8:263–8.

13.

Fenwick

Palmer

Claxton

Sculpher

Abrams

Sutton

. An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery. Med Decis Making. 2006;26:480–96.

14.

Keisler

Collier

Chu

Sinatra

Linkov

. Value of information analysis: the state of application. 2014;Environ Syst Decis. 34(1):3–23.

15.

Steuten

van de Wetering

Groothuis-Oudshoorn

Retèl

. A systematic and critical review of the evolving methods and applications of value of information in academia and practice. Pharmacoeconomics. 2013;31(1):25–48.

16.

Baio

. Bayesian methods in health economics. Boca Raton (FL): CRC Press;2012.

17.

Campbell

McQueen

Libby

Spackman

Carlson

Briggs

. Cost-effectiveness uncertainty analysis methods a comparison of one-way sensitivity, analysis of covariance, and expected value of partial perfect information. Med Decis Making. 2014;35(5):596–607.

18.

Meltzer

. Addressing uncertainty in medical cost-effectiveness analysis: implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to set priorities for medical research. J Health Econ. 2001;20(1):109–29.

19.

Wilson

. A practical guide to value of information analysis. Pharmacoeconomics. 2015;33(2):105–21.

20.

Brennan

Kharroubi

O’Hagan

Chilcott

. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms. Med Decis Making. 2007;27:448–70.

21.

Madan

Ades

Price

Maitland

Jemutai

Revill

Welton

. Strategies for efficient computation of the expected value of partial perfect information. Med Decis Making. 2014;34(3):327–42.

22.

Oakley

. Value of information for complex cost-effectiveness models. Sheffield (UK): University of Sheffield;2002.

23.

Sadatsafavi

Bansback

Zafari

Najafzadeh

Marra

. Need for speed: an efficient algorithm for calculation of single-parameter expected value of partial perfect information. Value Health. 2013;16(2):438–48.

24.

Strong

Oakley

. An efficient method for computing single-parameter partial expected value of perfect information. Med Decis Making. 2013;33(6):755–66.

25.

Strong

Oakley

Brennan

. Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach. Med Decis Making. 2014;34(3):311–26.

26.

Coyle

Oakley

. Estimating the expected value of partial perfect information: a review of methods. Eur J Health Econ. 2008;9(3):251–259.

27.

Brennan

Chilcott

Kharroubi

O’Hagan

. Calculating expected value of perfect information: resolution of conflicting methods via a two level Monte Carlo approach. In: Proceedings of the 24th Annual Meeting of SMDM, Washington, 2002.

28.

Strong

. Partial EVPPI functions. Available from: URL: http://www.shef.ac.uk/polopoly_fs/1.305039!/file/R_functions.txt

29.

Hastie

Tibshirani

. Generalized additive models (Vol. 43). Boca Raton (FL): CRC Press;1990.

30.

Rasmussen

Williams

. Gaussian processes for machine learning. Cambridge (MA): MIT Press;2006.

31.

Strong

Breeze

Thomas

Brennan

. SAVI—Sheffield Accelerated Value of Information. Release version 1.013 (2014 Dec 11). Sheffield (UK): University of Sheffield;2014.

32.

Baio

. BCEA: An R package to run Bayesian cost-effectiveness analysis. Available from: URL: http://www.statistica.it/gianluca/BCEA/BCEA.pdf

33.

Baio

Berardi

Heath

. Bayesian cost effectiveness analysis with the R package BCEA. New York: Springer; Forthcoming.

34.

Strong

Oakley

Brennan

Breeze

. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample a fast nonparametric regression-based method. Med Decis Making. 2015;35(5):570–83.

35.

Baio

Dawid

. Probabilistic sensitivity analysis in health economics. Stat Methods Med Res. 2011;24(6):615–34.

36.

Cooper

Sutton

Abrams

Turner

Wailoo

. Comprehensive decision analytical modelling in economic evaluation: a Bayesian approach. Health Econ. 2004;13(3):203–26.

37.

Heath

Manolopoulou

Baio

. Estimating the expected value of partial perfect information in health economic evaluations using integrated nested laplace approximation. Stat Med. 2016;35(23):4264–80.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.12 MB

0.17 MB

0.05 MB

0.06 MB

A Review of Methods for Analysis of the Expected Value of Information

Abstract

Keywords

Get full access to this article

References

Supplementary Material