## PM15 VALUE OF INFORMATION ANALYSIS USING REGRESSION METAMODELING

Sunday, October 18, 2015: 2:00 PM - 5:30 PM
Mills Studio 3 (Hyatt Regency St. Louis at the Arch)
Course Director:

Course Faculty:

Course Type: Half Day
Course Limit: 40

Overview: In this course, participants will learn the theory and application of value of information analysis (VOI) using a highly efficient regression metamodeling algorithm and a dataset of probabilistic sensitivity analyses.

Background: Value of information analysis (VOI) is a key concept in decision analyses. VOI is useful in sensitivity analysis, informing study design and resource allocation for further research. However, traditional methods of conducting VOI are often computationally demanding. In this course, participants will learn how to compute various measures of VOI using a single dataset of probabilistic sensitivity analyses and an efficient regression metamodeling approach in R.

Format Requirements: This course consists of lectures explaining the theory of VOI analysis interspaced with “hands-on” experience calculating various measures of value of information (VOI) analyses using linear regression metamodeling. Participants will work through structured examples using their own computers. Data sets and files needed for the course will be distributed during the course session. A basic level experience with probabilistic sensitivity analysis, regression and VOI are preferred, but not required.

Description and Objectives: The purpose of this course is to familiarize students with the concept and application of VOI using linear regression metamodeling. By the end of this course, participants will gain hands-on-experience calculating various measures of VOI, including:

• Expected value of perfect information (EVPI): The value of eliminating all sources of parameter uncertainties in a model.
• Expected value of partial perfect information (EVPPI): The value of eliminating uncertainty for one or more parameters accounting for possible correlation.
• Expected value of sample information (EVSI): The value of collecting additional information of all parameters of the model for a given sample size (n).
• Expected value of partial sample information (EVPSI): The value of collecting additional information on one or more parameters accounting for possible correlation for a given n.
• Expected net benefit of sampling (ENBS): The benefit of collecting additional information after accounting for the cost of research for a range of sample sizes and alternative research study designs.
• Optimal sample size (n*):  The maximum number of patients in a research study design that provides the highest ENBS.

In addition, we will provide the R code to produce publication quality figures and tables for these measures.

Course Director:

Hawre Jalal, MD, MSc, PhD
University of Pittsburgh
Assistant Professor

Course Faculty:

Fernando Alarid-Escudero, MS
University of Minnesota