Bennette Caroline S, Veenstra David L, Basu Anirban, Baker Laurence H, Ramsey Scott D, Carlson Josh J
Departments of Pharmacy, University of Washington, Seattle, Washington (CSB, DLV, JJC),
Washington Health Services, University of Washington, Seattle, Washington (AB)
Med Decis Making. 2016 Jul;36(5):641-51. doi: 10.1177/0272989X16636847. Epub 2016 Mar 24.
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.
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.
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.
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.
信息价值(VOI)分析可使研究与对患者预后具有最大潜在影响的领域保持一致,但对于这些方法用于指导优先级决策的可行性和可接受性仍存在疑问。我们的目标是开发一种“实时”计算VOI的流程,为大型癌症临床试验组SWOG内的试验资金决策提供依据。
我们使用2008年至2013年间SWOG领导层审查的来自乳腺、胃肠道和泌尿生殖系统疾病委员会的9项随机II/III期试验提案的选定样本,开发了一种高效且可扩展的VOI建模方法。在整个建模开发过程中,SWOG研究人员与研究团队之间进行了双向沟通。使用蒙特卡罗模拟计算由拟议试验的主要终点评估的治疗效果的样本信息部分期望值。
我们从样本量计算中得出治疗效果估计的先验不确定性。我们的流程对于9项试验提案中的8项是可行且高效的:1名研究人员评估每项提案所需时间<1周。我们主要通过将VOI指标解构为预期健康益处和增量医疗成本,并假设我们模拟中的治疗决策基于健康益处来纳入利益相关者的意见。经过定制后,200多名SWOG成员对整体VOI框架、具体回顾性结果以及VOI分析为未来试验提案评估提供信息的潜力给予了积极反馈。
我们开发了一种高效且定制的流程来计算癌症临床试验的预期VOI,该流程在决策中可行且为研究人员所接受。目前SWOG正在对该方法进行前瞻性应用和评估。