Society for Medical Decision Making

Session Summary

This course presents computation methods for the Expected Value of Sample Information (EVSI), a decision-theoretic measure of the monetary value of collecting additional information through potential future research. Participants will discuss EVSI and how it can be used to design research studies. The course will then give a demonstration of how to efficiently compute EVSI in practice with accompanying code provided in R.

Description & Objectives

The purpose of this course is to introduce EVSI as a tool for research prioritization and study design. The course will introduce several recent methods for the calculation of EVSI alongside R code to calculate and present these measures. By the end of the course, participants will be able to:
• Distinguish four recently developed calculation methods for EVSI
• Decide which EVSI calculation method is suitable for a given health economic decision model
• Calculate EVSI in R for two different health economic models
• Present EVSI analyses using standardized, publication-quality graphics
• Discuss key assumptions for calculating the Expected Net Benefit of Sampling (ENBS)
• Design efficient future research studies by determining their optimal sample sizes

Learning Objectives

Introduce EVSI as a tool for research prioritization and study design; introduce several recent methods for the calculation of EVSI alongside R code; choose which EVSI calculation method is suitable for a given health economic decision model; calculate EVSI in R for two different health economic models; present EVSI analyses using standardized, publication-quality graphics; discuss key assumptions for calculating the Expected Net Benefit of Sampling (ENBS); design efficient future research studies by determining their optimal sample sizes

Pre-Course Preparation

Participants require experience using R and knowledge of probabilistic health economic modelling and prior exposure to the Value of Information analysis. Some knowledge of Bayesian statistical methods is helpful but not required.

Time Allocation & Topic Outline

Time Section
14:00-14:10 Introductions (Ask everyone to introduce themselves)
14:10-14:40 Review of NMB, PSA, EVPI, EVPPI
14:40-14:55 Designing future research
14:55-15:15 Definition EVSI/ENBS
15:15-15:30 Intro to Methods to Compute EVSI
15:30-15:50 Break
15:50-16:10 Chemotherapy Example + Code
16:10-16:40 Regression Based Method + Example
16:40-17:10 Moment Matching Method + Example
17:10-17:30 Strengths and Limitations

Faculty Background & Qualifications

Natalia Kunst, Centre for Health Economics, University of York.

Jeremy Goldhaber-Fiebert, Centers for Health Policy and Primary Care and Outcomes Research, Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA.

Fernando Alarid-Escudero, Centers for Health Policy and Primary Care and Outcomes Research, Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, Stanford, CA.

Hawre Jalal, School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, CA.

The faculty members are decision scientists with extensive experience in decision-analytic modeling and value of information analysis. They are part of the Collaborative Network for Value of Information Analysis, which is a group of over 20 researchers working to improve the calculation, adoption and application of Value of Information (VoI) methods in clinical and public health research.

COI
No conflict of interest