My research is methodologically oriented and centered around two axes. The first involves my main field of expertise, which is the design and analysis of discrete choice experiments (DCEs) to quantify people’s preferences. The second axis involves the development and application of more general econometric and data science methods in health. Experiments are an attractive tool for inferring monetary value through (in)direct statements of preferences. DCEs are arguably the most popular method currently used in preference and willingness to pay (WTP) elicitation studies, both in hypothetical and non-hypothetical settings. Originally, in the 1970s, the method was developed for marketing and transport studies, but in the last two decennia, it has spread to environmental and resource economics, agricultural and food economics, and health economics. The ever growing body of literature on DCEs emphasizes the increasing role they are playing. In a DCE respondents are generally asked to make choices between multiple alternatives, also called profiles, which are described by a number of attributes with different levels. Consequently, through nonlinear regression models, the utility each attribute (level) contributes to the good or service under study can be quantified and translated into (marginal) WTP. I have been a pioneer in the development of Bayesian D-optimal designs with partial profiles that allow the study of many attributes in a DCE, while at the same time keeping the choice tasks simple for the respondents. Partial profile designs unite these two –at first sight– conflicting goals and are therefore smart designs. Also, I have developed these designs using the Bayesian D-optimal methodology, which I have improved extensively and has become the state of the art. The adjective ‘Bayesian’ signifies that prior information concerning the respondents’ preferences is taken into account when designing a DCE. The adjective ‘optimal’ is used because the alternatives or profiles appearing in the choice situations are selected so that, roughly speaking, the statistical model and quantities such as WTP can be estimated with maximum precision. Results of DCEs that I have executed in collaboration with practitioners have easily found their way to the popular press. For example, a DCE collaboration in health with the Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID) of the University of Antwerp to determine the kinds of medical interventions Belgian citizens like to see reimbursed by the government, received much media attention through the press bulletin “Who is old and sick must suffer” in the Flemish newspaper De Standaard of June 18, 2014 and through the interview “Who is sick through their own fault should not count on compassion” in the program “De Ochtend” on Radio 1 on the same day. Coincidently, around the same time, a DCE similar to ours on the measurement of societal preferences for different criteria for reimbursement of medical interventions by the government had been performed by the Belgian Health Care Knowledge Center (KCE) who invited me to act as an external validator on their scientific report which was broadly published on 22 December 2014. Beyond DCEs I have also developed new indices to measure the correlation between socioeconomic conditions and health outcomes as well as new regression methods to decompose these indices. Recently, for example, I have adopted a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. Such approach proves useful because the difference in the effect of income on health between a low and a high income is found to be stronger for people on the lower health spectrum than would be portrayed by mean-based regression techniques. Note that any other type of analytical, econometric or data science problem also fits my research interest and is highly welcomed.
AbstractEconomic values are usually revealed in the market place. However, no such mechanism exists to reveal people's relative values for goods and services that are currently not being bought and sold in the marketplace. Still, scientists would like to know the monetary value people attribute to them. We want to be able to carry out cost-benefit analysis to determine the welfare effects of technological innovations or public policy, to forecast new product success, and to understand the degree to which behavior is consistent with preferences and beliefs. Choice experiments (CEs) are arguably the most popular method currently used in preference and willingness to pay (WTP) elicitation studies, both in hypothetical and non-hypothetical settings. Originally, the method was developed for marketing and transport studies, but in the last two decennia, it has spread to environmental and resource economics, agricultural and food economics, and health economics. The ever growing body of literature on CEs emphasizes the increasing role they are playing. In this elicitation method, respondents are generally asked to make choices between multiple alternatives, also called profiles, which are described by a number of attributes with different levels. Consequently, through nonlinear regression models, generally based on random utility theory (RUT), the utility each attribute (level) contributes to the good or service under study can be quantified and translated into (marginal) willingness to pay. To a large extent, the design of the CE drives the precision and the validity of the conclusions and it is therefore considered to be a key aspect of the planning of a CE. Designing a CE involves selecting the profiles to be used in the experiment The current state of the art is the Bayesian optimal design method. However, the design and analysis methods for CEs are constantly improving, which goes along with the improvement of the discrete choice models and the increasing number of applications in different fields. Research on empirical and methodological advances in CEs faces the following challenges. First, RUT assumes the respondent to act in a fully compensatory manner based on stable preferences. This has been found to be a demanding assumption. Hence, it is up to empirical research to determine what causes these assumptions to be violated and how sensitive the obtained estimates are to them. Second, the debate concerning what drives (out) hypothetical bias, being the difference between what people say they are willing to pay in a hypothetical survey question and what they will actually pay in a non-hypothetical experiment when money is really on the line or in real-life situations, has not been closed. Third, most CEs are hitherto single-site and/or single-case studies. By consequence, spatial and socio-cultural effects are often ignored, which impedes generalization. Despite the vast amount of studies, findings often remain context-specific and cross-case comparisons are limited. Researchers from various applied economic disciplines continuously keep improving the way of designing, collecting and analyzing choice data in search of behavioral insights as well as efficient policy development. While some informal connections between several of the participating groups are already in place, a more formal setup would provide a driving force for more rapid knowledge dissemination and state of the art development of expertise. Therefore, it is important for Flanders to create a united and multi-disciplinary platform to keep up to date with the latest developments on CEs and to gather sufficient critical mass to be able to compete with other consortia for publications and project funding. Moreover, with this scientific research network, we aim to provide a platform for postdoctoral researchers to exchange knowledge and to more easily and intensively collaborate intra- and internationally.
AbstractIn the context of the government contract 'Establishing a decision method for vaccination policy in Flanders' issued by the Flemish Agency for Care and Health, all statistical aspects will be performed of the discrete choice experiment that will be conducted among the Flemish population and among vaccination experts. This includes (1) the design of the experiment, (2) the cleaning of the data sets that are retrieved, (3) the analysis of the resulting data sets, (4) the formulation of conclusions, (5) summarizing and writing down the studies in one or more reports and (6) developing these reports into international publications in renowned journals. For the interpretation of the discrete choice experiment, reference is made to the technical file with specification N° AP/IZ-VAC/2018/2.
- Promotor: Kessels Roselinde