Literature DB >> 18480035

The option value of delay in health technology assessment.

Simon Eckermann1, Andrew R Willan.   

Abstract

UNLABELLED: Processes of health technology assessment (HTA) inform decisions under uncertainty about whether to invest in new technologies based on evidence of incremental effects, incremental cost, and incremental net benefit monetary (INMB). An option value to delaying such decisions to wait for further evidence is suggested in the usual case of interest, in which the prior distribution of INMB is positive but uncertain.
METHODS: of estimating the option value of delaying decisions to invest have previously been developed when investments are irreversible with an uncertain payoff over time and information is assumed fixed. However, in HTA decision uncertainty relates to information (evidence) on the distribution of INMB. This article demonstrates that the option value of delaying decisions to allow collection of further evidence can be estimated as the expected value of sample of information (EVSI). For irreversible decisions, delay and trial (DT) is demonstrated to be preferred to adopt and no trial (AN) when the EVSI exceeds expected costs of information, including expected opportunity costs of not treating patients with the new therapy. For reversible decisions, adopt and trial (AT) becomes a potentially optimal strategy, but costs of reversal are shown to reduce the EVSI of this strategy due to both a lower probability of reversal being optimal and lower payoffs when reversal is optimal. Hence, decision makers are generally shown to face joint research and reimbursement decisions (AN, DT and AT), with the optimal choice dependent on costs of reversal as well as opportunity costs of delay and the distribution of prior INMB.

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Year:  2008        PMID: 18480035     DOI: 10.1177/0272989X07312477

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  14 in total

Review 1.  Sample size determination for cost-effectiveness trials.

Authors:  Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2011-11       Impact factor: 4.981

2.  Assessing differences between physicians' realized and anticipated gains from electronic health record adoption.

Authors:  Lori T Peterson; Eric W Ford; John Eberhardt; Timothy R Huerta; Nir Menachemi
Journal:  J Med Syst       Date:  2009-08-08       Impact factor: 4.460

3.  The value of value of information: best informing research design and prioritization using current methods.

Authors:  Simon Eckermann; Jon Karnon; Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2010       Impact factor: 4.981

4.  Value of information and pricing new healthcare interventions.

Authors:  Andrew R Willan; Simon Eckermann
Journal:  Pharmacoeconomics       Date:  2012-06-01       Impact factor: 4.981

5.  Presenting evidence and summary measures to best inform societal decisions when comparing multiple strategies.

Authors:  Simon Eckermann; Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2011-07       Impact factor: 4.981

6.  A practical guide to value of information analysis.

Authors:  Edward C F Wilson
Journal:  Pharmacoeconomics       Date:  2015-02       Impact factor: 4.981

Review 7.  A systematic and critical review of the evolving methods and applications of value of information in academia and practice.

Authors:  Lotte Steuten; Gijs van de Wetering; Karin Groothuis-Oudshoorn; Valesca Retèl
Journal:  Pharmacoeconomics       Date:  2013-01       Impact factor: 4.981

Review 8.  Optimal global value of information trials: better aligning manufacturer and decision maker interests and enabling feasible risk sharing.

Authors:  Simon Eckermann; Andrew R Willan
Journal:  Pharmacoeconomics       Date:  2013-05       Impact factor: 4.981

9.  Can the real opportunity cost stand up: displaced services, the straw man outside the room.

Authors:  Simon Eckermann; Brita Pekarsky
Journal:  Pharmacoeconomics       Date:  2014-04       Impact factor: 4.981

10.  Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample: A Fast, Nonparametric Regression-Based Method.

Authors:  Mark Strong; Jeremy E Oakley; Alan Brennan; Penny Breeze
Journal:  Med Decis Making       Date:  2015-03-25       Impact factor: 2.583

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