Literature DB >> 18179665

Time and expected value of sample information wait for no patient.

Simon Eckermann1, Andrew R Willan.   

Abstract

OBJECTIVE: The expected value of sample information (EVSI) from prospective trials has previously been modeled as the product of EVSI per patient, and the number of patients across the relevant time horizon less those "used up" in trials. However, this implicitly assumes the eligible patient population to which information from a trial can be applied across a time horizon are independent of time for trial accrual, follow-up and analysis.
METHODS: This article demonstrates that in calculating the EVSI of a trial, the number of patients who benefit from trial information should be reduced by those treated outside as well as within the trial over the time until trial evidence is updated, including time for accrual, follow-up and analysis.
RESULTS: Accounting for time is shown to reduce the eligible patient population: 1) independent of the size of trial in allowing for time of follow-up and analysis, and 2) dependent on the size of trial for time of accrual, where the patient accrual rate is less than incidence. Consequently, the EVSI and expected net gain (ENG) at any given trial size are shown to be lower when accounting for time, with lower ENG reinforced in the case of trials undertaken while delaying decisions by additional opportunity costs of time.
CONCLUSIONS: Appropriately accounting for time reduces the EVSI of trial design and increase opportunity costs of trials undertaken with delay, leading to lower likelihood of trialing being optimal and smaller trial designs where optimal.

Entities:  

Mesh:

Year:  2007        PMID: 18179665     DOI: 10.1111/j.1524-4733.2007.00296.x

Source DB:  PubMed          Journal:  Value Health        ISSN: 1098-3015            Impact factor:   5.725


  17 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.  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

3.  Value of information and pricing new healthcare interventions.

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

4.  Using value-of-information methods when the disease is rare and the treatment is expensive--the example of hemophilia A.

Authors:  Lusine Abrahamyan; Andrew R Willan; Joseph Beyene; Marjorie Mclimont; Victor Blanchette; Brian M Feldman
Journal:  J Gen Intern Med       Date:  2014-08       Impact factor: 5.128

5.  The Curve of Optimal Sample Size (COSS): A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis.

Authors:  Eric Jutkowitz; Fernando Alarid-Escudero; Karen M Kuntz; Hawre Jalal
Journal:  Pharmacoeconomics       Date:  2019-07       Impact factor: 4.981

Review 6.  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 7.  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

8.  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

9.  Development and Evaluation of an Approach to Using Value of Information Analyses for Real-Time Prioritization Decisions Within SWOG, a Large Cancer Clinical Trials Cooperative Group.

Authors:  Caroline S Bennette; David L Veenstra; Anirban Basu; Laurence H Baker; Scott D Ramsey; Josh J Carlson
Journal:  Med Decis Making       Date:  2016-03-24       Impact factor: 2.583

10.  Is a comparative clinical trial for breast cancer tumor markers to monitor disease recurrence warranted? A value of information analysis.

Authors:  Rahber Thariani; Norah Lynn Henry; Scott D Ramsey; David K Blough; Bill Barlow; Julie R Gralow; David L Veenstra
Journal:  J Comp Eff Res       Date:  2013-05       Impact factor: 1.744

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.