A Review of Methods for the Analysis of the Expected Value of Information

TL;DR

This paper reviews recent methods for estimating the Expected Value of Partial Perfect Information (EVPPI) in health economics, highlighting the most efficient non-parametric regression approach and its practical implications.

Contribution

It provides a comprehensive overview of new EVPPI estimation methods, including technical details and a comparative case study.

Findings

Non-parametric regression offers the most efficient EVPPI estimation.

Recent methods enable practical use of EVPPI in health economic evaluations.

All methods are implemented in R for accessibility.

Abstract

Over recent years Value of Information analysis has become more widespread in health-economic evaluations, specifically as a tool to perform Probabilistic Sensitivity Analysis. This is largely due to methodological advancements allowing for the fast computation of a typical summary known as the Expected Value of Partial Perfect Information (EVPPI). A recent review discussed some estimations method for calculating the EVPPI but as the research has been active over the intervening years this review does not discuss some key estimation methods. Therefore, this paper presents a comprehensive review of these new methods. We begin by providing the technical details of these computation methods. We then present a case study in order to compare the estimation performance of these new methods. We conclude that the most recent development based on non-parametric regression offers the best method for calculating the EVPPI efficiently. This means that the EVPPI can now be used practically in health economic evaluations, especially as all the methods are developed in parallel with R