Caroline S Bennette1, David L Veenstra1, Anirban Basu2, Laurence H Baker3, Scott D Ramsey4, Josh J Carlson1. 1. Departments of Pharmacy, University of Washington, Seattle, Washington (CSB, DLV, JJC), 2. Washington Health Services, University of Washington, Seattle, Washington (AB) 3. University of Michigan, Ann Arbor, Michigan (LHB) 4. Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center, Seattle, Washington (SDR).
Abstract
OBJECTIVE: Value of information (VOI) analyses can align research with areas with the greatest potential impact on patient outcome, but questions remain concerning the feasibility and acceptability of these approaches to inform prioritization decisions. Our objective was to develop a process for calculating VOI in "real time" to inform trial funding decisions within SWOG, a large cancer clinical trials group. METHODS: We developed an efficient and scalable VOI modeling approach using a selected sample of 9 randomized phase II/III trial proposals from the Breast, Gastrointestinal, and Genitourinary Disease Committees reviewed by SWOG's leadership between 2008 and 2013. There was bidirectional communication between SWOG investigators and the research team throughout the modeling development. Partial expected value of sample information for the treatment effect evaluated by the proposed trial's primary endpoint was calculated using Monte Carlo simulation. RESULTS: We derived prior uncertainty in the treatment effect estimate from the sample size calculations. Our process was feasible for 8 of 9 trial proposals and efficient: the time required of 1 researcher was <1 week per proposal. We accommodated stakeholder input primarily by deconstructing VOI metrics into expected health benefits and incremental healthcare costs and assuming treatment decisions within our simulations were based on health benefits. Following customization, feedback from over 200 SWOG members was positive regarding the overall VOI framework, specific retrospective results, and potential for VOI analyses to inform future trial proposal evaluations. CONCLUSIONS: We developed an efficient and customized process to calculate the expected VOI of cancer clinical trials that is feasible for use in decision making and acceptable to investigators. Prospective use and evaluation of this approach is currently underway within SWOG.
OBJECTIVE: Value of information (VOI) analyses can align research with areas with the greatest potential impact on patient outcome, but questions remain concerning the feasibility and acceptability of these approaches to inform prioritization decisions. Our objective was to develop a process for calculating VOI in "real time" to inform trial funding decisions within SWOG, a large cancer clinical trials group. METHODS: We developed an efficient and scalable VOI modeling approach using a selected sample of 9 randomized phase II/III trial proposals from the Breast, Gastrointestinal, and Genitourinary Disease Committees reviewed by SWOG's leadership between 2008 and 2013. There was bidirectional communication between SWOG investigators and the research team throughout the modeling development. Partial expected value of sample information for the treatment effect evaluated by the proposed trial's primary endpoint was calculated using Monte Carlo simulation. RESULTS: We derived prior uncertainty in the treatment effect estimate from the sample size calculations. Our process was feasible for 8 of 9 trial proposals and efficient: the time required of 1 researcher was <1 week per proposal. We accommodated stakeholder input primarily by deconstructing VOI metrics into expected health benefits and incremental healthcare costs and assuming treatment decisions within our simulations were based on health benefits. Following customization, feedback from over 200 SWOG members was positive regarding the overall VOI framework, specific retrospective results, and potential for VOI analyses to inform future trial proposal evaluations. CONCLUSIONS: We developed an efficient and customized process to calculate the expected VOI of cancer clinical trials that is feasible for use in decision making and acceptable to investigators. Prospective use and evaluation of this approach is currently underway within SWOG.
Authors: Patricia A Deverka; Danielle C Lavallee; Priyanka J Desai; Laura C Esmail; Scott D Ramsey; David L Veenstra; Sean R Tunis Journal: J Comp Eff Res Date: 2012-03 Impact factor: 1.744
Authors: William B Wong; Scott D Ramsey; William E Barlow; Louis P Garrison; David L Veenstra Journal: Contemp Clin Trials Date: 2012-08-18 Impact factor: 2.226
Authors: Josh J Carlson; Rahber Thariani; Josh Roth; Julie Gralow; N Lynn Henry; Laura Esmail; Pat Deverka; Scott D Ramsey; Laurence Baker; David L Veenstra Journal: Med Decis Making Date: 2013-05 Impact factor: 2.583
Authors: Eric Jutkowitz; Fernando Alarid-Escudero; Hyon K Choi; Karen M Kuntz; Hawre Jalal Journal: Pharmacoeconomics Date: 2017-10 Impact factor: 4.981
Authors: David D Kim; Gregory F Guzauskas; Caroline S Bennette; Anirban Basu; David L Veenstra; Scott D Ramsey; Josh J Carlson Journal: Pharmacoeconomics Date: 2020-02 Impact factor: 4.981
Authors: Josh J Carlson; David D Kim; Gregory F Guzauskas; Caroline S Bennette; David L Veenstra; Anirban Basu; Nathaniel Hendrix; Dawn L Hershman; Laurence Baker; Scott D Ramsey Journal: Cancer Med Date: 2018-07-20 Impact factor: 4.452
Authors: Beth Woods; Laetitia Schmitt; Claire Rothery; Andrew Phillips; Timothy B Hallett; Paul Revill; Karl Claxton Journal: BMJ Glob Health Date: 2020-08