ArticleJournal of health economics2019
Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data.
Article in Journal of health economics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- User Perspectives on a Clinical Decision Tool to Support Individualized Exercise Prescriptions for Breast Cancer Survivors Not Meeting Exercise Guidelines: Cross-Sectional Survey.JMIR formative research · 2026Article
- The Risk-Benefit Balance of Oral Corticosteroid Treatment for Asthma Attacks: A Discrete Choice Experiment of Patients and Healthcare Professionals in the UK and New Zealand.Respirology (Carlton, Vic.) · 2025Article
- A Break from the Norm? Parametric Representations of Preference Heterogeneity for Discrete Choice Models in Health.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Article
- Exploring the Factors that Drive Clinical Negligence Claims: Stated Preferences of Those Who Have Experienced Unintended Harm.The patient · 2024Article
- Identifying Preferred Features of Weight Loss Programs for Adults With or at Risk of Type 2 Diabetes: A Discrete Choice Experiment With 3,960 Adults in the U.K.Diabetes care · 2024Article
- The Economics of Tobacco Regulation: A Comprehensive Review.Journal of economic literature · 2022Article
- The demand for a COVID-19 vaccine.Economics and human biology · 2022Article
- Impact of Little Cigars and Cigarillos Packaging Features on Product Preference.International journal of environmental research and public health · 2021Article
- Strategies to increase downloads of COVID-19 exposure notification apps: A discrete choice experiment.PloS one · 2021Article
- Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior.Health economics · 2021Article
- Mostly harmless regulation? Electronic cigarettes, public policy, and consumer welfare.Health economics · 2020Article
- Measuring commissioners' willingness-to-pay for community based childhood obesity prevention programmes using a discrete choice experiment.BMC public health · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.