This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (43) Citing Articles via Google Scholar Google Scholar Articles by Ades, A. E. Articles by Claxton, K. Search for Related Content PubMed PubMed Citation Articles by Ades, A. E. Articles by Claxton, K. Social Bookmarking                   What's this?

Expected Value of Sample Information Calculations in Medical Decision Modeling

Medical Research Council Health Services Research Collaboration, Bristol, United Kingdom

G. Lu, PhD

Medical Research Council Health Services Research Collaboration, Bristol, United Kingdom

K. Claxton, PhD

Department of Economics and Related Studies, University of York, United Kingdom

There has been an increasing interest in using expected value of information (EVI) theory in medical decision making, to identify the need for further research to reduce uncertainty in decision and as a tool for sensitivity analysis. Expected value of sample information (EVSI) has been proposed for determination of optimum sample size and allocation rates in randomized clinical trials. This article derives simple Monte Carlo, or nested Monte Carlo, methods that extend the use of EVSI calculations to medical decision applications with multiple sources of uncertainty, with particular attention to the form in which epidemiological data and research findings are structured. In particular, information on key decision parameters such as treatment efficacy are invariably available on measures of relative efficacy such as risk differences or odds ratios, but not on model parameters themselves. In addition, estimates of model parameters and of relative effect measures in the literature may be heterogeneous, reflecting additional sources of variation besides statistical sampling error. The authors describe Monte Carlo procedures for calculating EVSI for probability, rate, or continuous variable parameters in multi parameter decision models and approximate methods for relative measures such as risk differences, odds ratios, risk ratios, and hazard ratios. Where prior evidence is based on a random effects meta-analysis, the authors describe different ESVI calculations, one relevant for decisions concerning a specific patient group and the other for decisions concerning the entire population of patient groups. They also consider EVSI methods for new studies intended to update information on both baseline treatment efficacy and the relative efficacy of 2 treatments. Although there are restrictions regarding models with prior correlation between parameters, these methods can be applied to the majority of probabilistic decision models. Illustrative worked examples of EVSI calculations are given in an appendix.

Key Words: expected value of sample information • epidemiology • odds ratios • random effects

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				Z. Philips, K. Claxton, and S. Palmer The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions? Med Decis Making, June 1, 2008; 28(3): 287 - 299. [Abstract] [PDF]

				E. Fenwick, K. Claxton, and M. Sculpher The Value of Implementation and the Value of Information: Combined and Uneven Development Med Decis Making, February 1, 2008; 28(1): 21 - 32. [Abstract] [PDF]

				L. Bojke, K. Claxton, M. J. Sculpher, and S. Palmer Identifying Research Priorities: The Value of Information Associated with Repeat Screening for Age-Related Macular Degeneration Med Decis Making, February 1, 2008; 28(1): 33 - 43. [Abstract] [PDF]

				B. Groot Koerkamp, J. J. Nikken, E. H. Oei, T. Stijnen, A. Z. Ginai, and M. G. M. Hunink Value of Information Analysis Used to Determine the Necessity of Additional Research: MR Imaging in Acute Knee Trauma as an Example Radiology, February 1, 2008; 246(2): 420 - 425. [Abstract] [Full Text] [PDF]

				T. E Colbourn, C. Asseburg, L. Bojke, Z. Philips, N. J Welton, K. Claxton, A E Ades, and R. E Gilbert Preventive strategies for group B streptococcal and other bacterial infections in early infancy: cost effectiveness and value of information analyses BMJ, September 29, 2007; 335(7621): 655 - 655. [Abstract] [Full Text] [PDF]

				B. Groot Koerkamp, M.G. M. Hunink, T. Stijnen, J. K. Hammitt, K. M. Kuntz, and M. C. Weinstein Limitations of Acceptability Curves for Presenting Uncertainty in Cost-Effectiveness Analysis Med Decis Making, March 1, 2007; 27(2): 101 - 111. [Abstract] [PDF]

				D. M. Epstein, Z. Chalabi, K. Claxton, and M. Sculpher Efficiency, Equity, and Budgetary Policies: Informing Decisions Using Mathematical Programming Med Decis Making, March 1, 2007; 27(2): 128 - 137. [Abstract] [PDF]

				A. J Girling, R. J Lilford, D. A Braunholtz, and W. R Gillett Sample-size calculations for trials that inform individual treatment decisions: a 'true-choice' approach Clinical Trials, February 1, 2007; 4(1): 15 - 24. [Abstract] [PDF]

				E. Fenwick, S. Palmer, K. Claxton, M. Sculpher, K. Abrams, and A. 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, September 1, 2006; 26(5): 480 - 496. [Abstract] [PDF]

				G. B. Hazen and M. Huang Large-sample bayesian posterior distributions for probabilistic sensitivity analysis. Med Decis Making, September 1, 2006; 26(5): 512 - 534. [Abstract] [PDF]

				A. E. Ades, G. Lu, and J. P. T. Higgins The Interpretation of Random-Effects Meta-Analysis in Decision Models Med Decis Making, November 1, 2005; 25(6): 646 - 654. [Abstract] [PDF]

				M. G. M. Hunink Decision Making in the Face of Uncertainty and Resource Constraints: Examples from Trauma Imaging Radiology, May 1, 2005; 235(2): 375 - 383. [Abstract] [Full Text] [PDF]

				J. Hilden and N. Obuchowski What Properties Should an Overall Measure of Test Performance Possess? * Dr. Obuchowski responds: Clin. Chem., February 1, 2005; 51(2): 471 - 472. [Full Text] [PDF]

				K. Claxton, J. T. Cohen, and P. J. Neumann When Is Evidence Sufficient? Health Aff., January 1, 2005; 24(1): 93 - 101. [Abstract] [Full Text] [PDF]