Discrete Choice Experiments in Health Care: theory and applications

Health economics is concerned with issues related to scarcity in the allocation of health care. The basic tasks of any economic evaluation are to identify, measure, value, and compare the costs and benefits of alternatives being considered. Traditional means of measuring benefits in the delivery of health care have concentrated on improvements in health outcomes using clinical outcomes and Quality Adjusted Life Years (QALY). The QALY is a measure of the quantity of life gained weighted by the quality of that life. QALYs are extensively used in economic analyses in health care. They claim to capture the health outcome benefits caused by an intervention . However, benefits of a health care intervention or service can be many-sided, e.g. containing non-health outcomes (e.g. amount of information) and process characteristics (e.g. treatment location, route of drug administration, patient experienced burden of testing). For instance, is ‘reduction of dying from cervical cancer’ the only screening characteristic that is considered by women attending a cervical cancer screening programme? Evidence shows that, within the context of cervical cancer screening, women’s preferences for various programmes are also determined by other characteristics than the reduced chance of dying from cervical cancer. Individuals are willing to trade changes in health outcome (change in chance of dying from cervical cance

Applications of discrete choice experiments in COVID-19 research:Disparity in survey qualities between health and transport fields

Published choice experiments linked to various aspects of the COVID-19 pandemic are analysed in a rapid review. The aim is to (i) document the diversity of topics as well as their temporal and geographical patterns of emergence, (ii) compare various elements of design quality across different sectors of applied economics, and (iii) identify potential signs of convergent validity across findings of comparable experiments. Of the N = 43 published choice experiments during the first two years of the pandemic, the majority identifies with health applications (n = 30), followed by transport-related applications (n = 10). Nearly 100,000 people across the world responded to pandemic-related discrete choice surveys. Within health applications, while the dominant theme, up until June 2020, was lockdown relaxation and tracing measures, the focus shifted abruptly to vaccine preference since then. Geographical origins of the health surveys were not diverse. Nearly 50% of all health surveys were conducted in only three countries, namely US, China and The Netherlands. Health applications exhibited stronger pre-testing and larger sample sizes compared to transport applications. Limited signs of convergent validity were identifiable. Within some applications, issues of temporal instability as well as hypothetical bias attributable to social desirability, protest response or policy consequentiality seemed likely to have affected the findings. Nevertheless, very few of the experiments implemented measures of hypothetical bias mitigation and those were limited to health studies. Our main conclusion is that swift administration of pandemic-related choice experiments has overall resulted in certain degrees of compromise in study quality, but this has been more so the case in relation to transport topics than health topics

An attempt to decrease social desirability bias:The effect of cheap talk mitigation on internal and external validity of discrete choice experiments

Discrete choice experiments (DCEs) have been used extensively to elicit preferences. However, the hypothetical nature of choices induces socially desirable behaviour and endangers internal and external validity of DCEs. This study experimentally investigated social desirability bias in DCEs and whether it can be mitigated using the cheap talk mitigation method in the context of food choices. Respondents (N = 1027) were randomly allocated to one of four questionnaire versions: default without manipulation, priming socially desirable behaviour, cheap talk mitigation, or both. The effect on internal validity was assessed by comparing respondent-reported characteristics, DCE results, and prediction accuracy for a holdout task between questionnaire versions. The effect on external validity was assessed by comparing stated and revealed preferences. Social desirability bias, if present, was hardly affected by cheap talk mitigation. Respondent-reported characteristics, DCE results and prediction accuracy for the holdout task and actual food choice did not strongly differ between questionnaire versions. Prediction accuracy for the holdout task was lowest in the default version. Prediction accuracy for actual food choice was slightly better among respondents in the versions that were exposed to cheap talk mitigation. Social desirability bias was hard to detect and mitigate in this study, potentially due to limited social desirability, the effectiveness of the cheap talk mitigation method, and other sources of hypothetical bias. The differences in prediction accuracy indicates that cheap talk mitigation slightly improved external validity at minimum cost to internal validity. Recommendations for future research are provided.</p

Public Preferences for Introducing a COVID-19 Certificate:A Discrete Choice Experiment in the Netherlands

Objective: Here we investigate public preferences for coronavirus disease 2019 (COVID-19) certificates in the Netherlands, and whether these preferences differ between subgroups in the population. Methods: A survey including a discrete choice experiment was administered to 1500 members of the adult population of the Netherlands. Each participant was asked to choose between hypothetical COVID-19 certificates that differed in seven attributes: the starting date, and whether the certificate allowed gathering with multiple people, shopping without appointment, visiting bars and restaurants, visiting cinemas and theatres, attending events, and practising indoor sports. Latent class models (LCMs) were used to determine the attribute relative importance and predicted acceptance rate of hypothetical certificates. Results: Three classes of preference patterns were identified in the LCM. One class a priori opposed a certificate (only two attributes influencing preferences), another class was relatively neutral and included all attributes in their decision making, and the final class was positive towards a certificate. Respondents aged &gt; 65 years and those who plan to get vaccinated were more likely to belong to the latter two classes. Being allowed to shop without appointment and to visit bars and restaurants was most important to all respondents, increasing predicted acceptance rate by 12 percentage points. Conclusions: Preferences for introduction of a COVID-19 certificate are mixed. A certificate that allows for shopping without appointment and visiting bars and restaurants is likely to increase acceptance. The support of younger citizens and those who plan to get vaccinated seems most sensitive to the specific freedoms granted by a COVID-19 certificate

Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide

Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice dat

Mimicking Real-Life Decision Making in Health: Allowing Respondents Time to Think in a Discrete Choice Experiment

Objective: To empirically test the impact of allowing respondents time to think (TTT) about their choice options on the outcomes of a discrete choice experiments (DCE). Methods: In total, 613 participants of the Swedish CArdioPulmonary bioImage Study (SCAPIS) completed a DCE questionnaire that measured their preferences for receiving secondary findings of a genetic test. A Bayesian D-efficient design with 60 choice tasks divided over 4 questionnaires was used. Each choice task contained 2 scenarios with 4 attributes: type of disease, disease penetrance probability, preventive opportunities, and effectiveness of prevention. Respondents were randomly allocated to the TTT or no TTT (NTTT) sample. Latent class models (LCMs) were estimated to determine attribute-level values and their relative importance. In addition, choice certainty, attribute-level interpretation, choice consistency, and potential uptake rates were compared between samples. Results: In the TTT sample, 92% of the respondents (245 of 267) indicated they used the TTT period to (1) read the information they received (72%) and (2) discuss with their family (24%). In both samples, respondents were very certain about their choices. A 3-class LCM was fitted for both samples. Preference reversals were found for 3 of the 4 attributes in one class in the NTTT sample (34% class-membership probability). Relative importance scores of the attributes differed between the 2 samples, and significant scale effects indicating higher choice consistency in TTT sample were found. Conclusions: Offering respondents TTT influences decision making and preferences. Developers of future DCEs regarding complex health-related decisions are advised to consider this approach to enhance the validity of the elicited preferences