Methods for the meta-analysis of willingness-to-pay data: an overview.
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 meta-analysis 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.
Van Houtven
George
G
RTI International, Research Triangle Park, North Carolina, USADepartment of Economics, Tilburg University, Tilburg, the Netherlands. gvh@rti.org
D003625:Data Collection / Q000379:methods; D017048:Health Care Costs; D005102:Health Expenditures; D006301:Health Services Needs and Demand / Q000191:economics; D006801:Humans; D015201:Meta-Analysis as Topic; D012044:Regression Analysis; D012107:Research Design