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Methods for the Meta-Analysis of Willingness-to-Pay Data

An Overview

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Abstract

Given the policy relevance and growing volume of research measuring individuals’ willingness to pay (WTP) for health-related goods and services, meta-analysis provides a potentially rich set of tools for answering key questions about this research area. In particular, when taken as a whole, what does the existing empirical literature tell us about health preferences, the effectiveness of health policies, and the demand for health-related goods and services? Although the application of meta-analysis techniques to health-related WTP data is fundamentally similar to other meta-analysis applications, it nonetheless presents a number of specific challenges. The purpose of this article is to describe some of the main features that distinguish WTP research and to discuss ways in which metaanalysis methods must be tailored to meet these challenges.

One of the most notable features of this research area is its heterogeneity in terms of research methods, reporting practices and publication outlets. This article discusses the implications of this diversity for the methods used at various stages of meta-analysis, including problem formulation, data collection, data evaluation and abstraction, data preparation and data analysis. One central implication is a strong reliance on meta-regression and panel data approaches. Another key feature is the frequent objective of providing benefit estimates for economic evaluation. The implication for meta-analysis is that it is a powerful tool not only for synthesizing results and testing hypotheses, but also for predicting WTP and generating benefit estimates for a variety of scenarios. This article discusses what this role implies for how meta-analysis is conducted and how the results are reported.

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Notes

  1. ‘Nonmarket valuation’ refers to the estimation of values for commodities, such as human health or environmental improvements, that are not typically exchanged in markets and whose prices are not directly observable. Values or implicit prices for these commodities must therefore be estimated using information from related or constructed markets.

  2. Hedonic wage-risk studies use regression methods to estimate how differences in occupational safety risks across job categories affect differences in wage compensation (controlling for other job characteristics).

  3. When the only difference between estimates is that some are from non-overlapping subsamples (e.g. for men and women separately) and others are for these subsamples combined, then it is arguably best in these cases to only include the subsample estimates (as long as the subsample differences can be accounted for with explanatory variables).

  4. For example, Stata’s random effects regression command (xtreg) does not allow for or include a regression weighting option.

  5. Examples of studies using this method are Poe et al.,[27] Smith and Osborne,[28] Van Houtven et al.[4] and Johnston et al.[29]

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Acknowledgements

The author is grateful to an anonymous referee who provided several insightful comments, to Subhrendu Pattanayak, John Powers and Sumeet Patil, who have greatly helped to shape my thinking on this topic.

No sources of funding were used to assist in the preparation of this article. The author has no conflicts of interest that are directly relevant to the content of this article.

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Authors and Affiliations

  1. RTI International, 3040 Cornwallis Road, PO Box 12194, Research Triangle Park, North Carolina, 27709, USA

    George Van Houtven

  2. Department of Economics, Tilburg University, Tilburg, the Netherlands

    George Van Houtven

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Correspondence to George Van Houtven.

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Van Houtven, G. Methods for the Meta-Analysis of Willingness-to-Pay Data. Pharmacoeconomics 26, 901–910 (2008). https://doi.org/10.2165/00019053-200826110-00003

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