Bayesian Cost-Effectiveness Analysis with the R package BCEA

Table of Contents

1. Frontmatter

2. Chapter 1. Bayesian Analysis in Health Economics

   Gianluca Baio, Andrea Berardi, Anna Heath, Nathan Green

   Abstract

   Modelling for economic evaluation of health-care data has received much attention in both the health economics and the statistical literature in recent years (Briggs et al., Decision modelling for health economic evaluation. Oxford University Press, 2006; Willan et al., The statistical analysis of cost-effectiveness data. Wiley, 2006), increasingly often under a Bayesian statistical approach (Baio, Bayesian methods in health economics. Chapman Hall/CRC Press, 2012; O’Hagan et al., Stat Med 20:733–753, 2001; O’Hagan and Stevens, Health Econ 10:303–315, 2001; Spiegelhalter et al., Bayesian approaches to clinical trials and health-care evaluation. Wiley, 2004).

3. Chapter 2. Case Studies

   Gianluca Baio, Andrea Berardi, Anna Heath, Nathan Green

   Abstract

   In this chapter, we present the two case studies that are used as running examples throughout the book.

4. Chapter 3. BCEA: An R Package for Bayesian Cost-Effectiveness Analysis

   Gianluca Baio, Andrea Berardi, Anna Heath, Nathan Green

   Abstract

   Cost-effectiveness analysis is usually performed using specialised software such as TreeAge or spreadsheet calculators (\(\text{e.g.}\)Microsoft Excel). Part of the narrative that accompanies this choice as the de facto standard is that these tools are “transparent and easy to use and to share with clients and stakeholders”.

5. Chapter 4. Probabilistic Sensitivity Analysis Using BCEA

   Gianluca Baio, Andrea Berardi, Anna Heath, Nathan Green

   Abstract

   Theoretically, as mentioned in Sect. 1.3, the maximisation of the expected utility is all that is required to determine the best course of action in the face of uncertainty and given current evidence (Baio, Bayesian methods in health economics. Chapman Hall/CRC Press, 2012; Baio and Dawid, Stat Methods Med Res 24, 2011; Claxton, J Health Econ 18:342–364, 1999). This means that if we completely trust all the assumptions made in the current modelling and the data used to inform the unobserved and unobservable quantities, then the computation of ICERs and EIBs would be sufficient to determine which treatment is the most cost-effective. The decision-making process would therefore be completely automated under these circumstances. This implies that, as shown throughout Chap. 3, the vaccination strategy and the Group Counselling interventions would be implemented immediately for willingness-to-pay values of 25,000 and 250 monetary units, respectively, in the two examples of Chap. 2.

6. Chapter 5. BCEAweb: A User-Friendly Web App to Use BCEA

   Gianluca Baio, Andrea Berardi, Anna Heath, Nathan Green

   Abstract

   In this chapter we introduce BCEAweb, a web interface for BCEA. BCEAweb is a web application aimed at everyone who does not use R to develop economic models and wants a user-friendly way to analyse both the assumptions and the results of a health economic evaluation. The results of any probabilistic model can be very easily imported into the web app, and the outcomes are analysed using a wide array of standardised functions. The chapter will introduce the use of the main functions of BCEAweb and how to use its capabilities to produce result summaries, tables, and graphs.

7. Backmatter