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Description
Location
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Session Details
AM05: Research Prioritisation and Study Design Using Value of Information Analysis
(
Event:
SMDM 41st Annual Meeting: Portland, OR)
Oct 20, 2019 9:00AM - Oct 20, 2019 12:30PM
Session Type:
Short Course- AM 1/2 Day
Description
Background
VOI encompasses a suite of measures that quantify the value of reducing parametric uncertainty within a health economic model. VOI measures can determine whether the current evidence base for a health economic decision model is sufficient to make policy decisions. They can also direct future research by determining the model inputs with the greatest influence on decision uncertainty. In addition to this, VOI measures can determine the optimal design for a research study. Despite this versatility, VOI has rarely been used in practice for research prioritization and study design. This is due to a lack of familiarity, difficulties interpreting these measures, concerns about the assumptions underpinning them and computational complexity. The Collaborative Network for Value of Information (ConVOI) group is an international team of experts in developing and applying cutting edge VOI methods that aim to address these challenges.
Course Type
Half Day
CourseLevel
Beginner
Format Requirements
This course is a mixture of informal lectures and discussion sessions. The lectures will present the definition of key VOI measures, discuss the assumptions underpinning VOI analyses and demonstrate a number of graphical displays for VOI measures. Participants will discuss and interpret examples of VOI analysis from the literature. We will provide R code for all examples so that participants can continue to explore what they learn during the course. Participants should have some knowledge of health economic evaluation/health technology assessment and Probabilistic Sensitivity Analysis (PSA).
Overview
Value of Information (VOI) is a key concept in decision analysis that can be used to determine research priorities, inform resource allocation for potential further research and design proposed research studies. This course will introduce the general concepts behind VOI, present several key VOI measures and highlight where they can be most useful in directing future research. It will also demonstrate key graphical presentations of these measures and critically evaluate VOI analyses and their underlying assumptions.
Description & Objectives
The purpose of this course is to introduce VOI measures and their use in decision modelling. The course will introduce these measures, discuss their presentation and assumptions. By the end of the course, participants will be able to
Interpret the Expected Value of Perfect Information (EVPI)
Interpret the Expected Value of Perfect Partial Information (EVPPI)
Interpret the Expected Value of Sample Information (EVSI)
Interpret the Expected Net Benefit of Sampling (ENBS)
Discuss key assumptions that impact a VOI analysis
Explore the results of a VOI analysis using graphical displays
Critically evaluate the assumptions underpinning VOI analyses
Use VOI analysis to determine research priorities and design clinical research.
Description
VOI encompasses a suite of measures that quantify the value of reducing parametric uncertainty within a health economic model. VOI measures can determine whether the current evidence base for a health economic decision model is sufficient to make policy decisions. They can also direct future research by determining the model inputs with the greatest influence on decision uncertainty. In addition to this, VOI measures can determine the optimal design for a research study. Despite this versatility, VOI has rarely been used in practice for research prioritization and study design. This is due to a lack of familiarity, difficulties interpreting these measures, concerns about the assumptions underpinning them and computational complexity. The Collaborative Network for Value of Information (ConVOI) group is an international team of experts in developing and applying cutting edge VOI methods that aim to address these challenges.
Course Directors
Jeremy D. Goldhaber-Fiebert
Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University
Anna Heath
The Hospital for Sick Children
Course Faculty
Hawre Jalal
University of Pittsburgh Graduate School of Public Health
Fernando Alarid-Escudero
Drug Policy Program, Center for Research and Teaching in Economics
Natalia R. Kunst
Yale University School of Medicine
Register for this Session
Session Fees
Fee Type
Member Fee
Non-Member Fee
This session is free
Early:
$204.00
$332.00
Regular:
$250.00
$378.00
Late:
$250.00
$378.00
This session is free
Early:
$174.00
$174.00
Regular:
$220.00
$220.00
Late:
$220.00
$220.00