The purpose of this course is to familiarize participants with the concepts and application of linear regression metamodeling in sensitivity analysis and VOI. By the end of this course, participants will gain hands-on-experience using linear regression metamodeling as a tool to produce a panel of sensitivity analyses as recommended by the SMDM-ISPOR Task Force and calculating various measures of VOI from a PSA dataset, including:

• One- and two-way parameter sensitivity analyses, and threshold analyses.
• 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 R code to produce publication quality figures and tables for these measures.