Anna Heath

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested laplace approximation

The Expected Value of Perfect Partial Information (EVPPI) is a decision‐theoretic measure of the ‘cost’ of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision‐theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non‐parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high‐dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high‐dimensional into a low‐dimensional input space allows us to decrease the computation time for fitting these high‐dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently.

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

In recent years, value-of-information analysis has become more widespread in health economic evaluations, specifically as a tool to guide further research and perform probabilistic sensitivity analysis. This is partly 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 approximation methods for calculating the EVPPI, but as the research has been active over the intervening years, that 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 two case studies in order to compare the estimation performance of these new methods. We conclude that a method based on nonparametric regression offers the best method for calculating the EVPPI in terms of accuracy, computational time, and ease of implementation. 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 functions and a web app to aid practitioners.

Bayesian Cost-Effectiveness Analysis with the R Package BCEA

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach.

Efficient Monte Carlo Estimation of the Expected Value of Sample Information Using Moment Matching

Background. The Expected Value of Sample Information (EVSI) is used to calculate the economic value of a new research strategy. Although this value would be important to both researchers and funders, there are very few practical applications of the EVSI. This is due to computational difficulties associated with calculating the EVSI in practical health economic models using nested simulations. Methods. We present an approximation method for the EVSI that is framed in a Bayesian setting and is based on estimating the distribution of the posterior mean of the incremental net benefit across all possible future samples, known as the distribution of the preposterior mean. Specifically, this distribution is estimated using moment matching coupled with simulations that are available for probabilistic sensitivity analysis, which is typically mandatory in health economic evaluations. Results. This novel approximation method is applied to a health economic model that has previously been used to assess the performance of other EVSI estimators and accurately estimates the EVSI. The computational time for this method is competitive with other methods. Conclusion. We have developed a new calculation method for the EVSI which is computationally efficient and accurate. Limitations. This novel method relies on some additional simulation so can be expensive in models with a large computational cost.

Calculating the Expected Value of Sample Information Using Efficient Nested Monte Carlo: A Tutorial

OBJECTIVE The expected value of sample information (EVSI) quantifies the economic benefit of reducing uncertainty in a health economic model by collecting additional information. This has the potential to improve the allocation of research budgets. Despite this, practical EVSI evaluations are limited partly due to the computational cost of estimating this value using the gold-standard nested simulation methods. Recently, however, Heath et al. developed an estimation procedure that reduces the number of simulations required for this gold-standard calculation. Up to this point, this new method has been presented in purely technical terms. STUDY DESIGN This study presents the practical application of this new method to aid its implementation. We use a worked example to illustrate the key steps of the EVSI estimation procedure before discussing its optimal implementation using a practical health economic model. METHODS The worked example is based on a three-parameter linear health economic model. The more realistic model evaluates the cost-effectiveness of a new chemotherapy treatment, which aims to reduce the number of side effects experienced by patients. We use a Markov model structure to evaluate the health economic profile of experiencing side effects. RESULTS This EVSI estimation method offers accurate estimation within a feasible computation time, seconds compared to days, even for more complex model structures. The EVSI estimation is more accurate if a greater number of nested samples are used, even for a fixed computational cost. CONCLUSIONS This new method reduces the computational cost of estimating the EVSI by nested simulation.

Probabilistic Sensitivity Analysis Using BCEA

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 [1, 2, 3].

Bayesian Analysis in Health Economics

Modelling for the economic evaluation of healthcare data has received much attention in both the health economics and the statistical literature in recent years [1, 2], increasingly often under a Bayesian statistical approach [3, 4, 5, 6].

BCEAweb : A User-Friendly Web-App to Use BCEA

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 an 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 results summaries, tables and graphs.

BCEA—A R Package for Bayesian Cost-Effectiveness Analysis

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

When simple becomes complicated: why Excel should lose its place at the top table

Traditionally, the majority of health economic modelling has been performed in spreadsheet calculators such as Microsoft Excel as it is perceived to be more transparent and easy to use. However, as the modelling requirements become more realistic and therefore complex, spreadsheets become increasingly cumbersome and difficult to manage. We argue that specialist statistical packages such as R should be used when the models become suitably complex. We acknowledge the difficulties associated with script-based statistical software, but argue that user-written packages designed for health-technology assessments simplify the analysis when compared to spreadsheet calculators. Additionally, we argue that the production of web-applications based on R will allow the statistical capabilities of specialist software to be available for all. All that is needed is a dialogue between the modellers and the academic to make the software available for all.