Related Experiment Video
Updated: May 11, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Random regret-based discrete-choice modelling: an application to healthcare
Esther W de Bekker-Grob1, Caspar G Chorus
1Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands. e.debekker@erasmusmc.nl
Pharmacoeconomics
|April 27, 2013
Summary
Researchers explored Random Regret Minimization (RRM) for healthcare choices, finding it offers policy-relevant insights. While model fit differences were small, RRM models revealed distinct attribute trade-offs compared to Random Utility Maximization (RUM).
Area of Science:
- Health economics
- Decision science
- Transport economics
Background:
- Discrete-choice experiments (DCEs) are analyzed using Random Utility Maximization (RUM).
- Random Regret Minimization (RRM) is a novel approach modeling regret minimization in choice behavior.
- RRM offers an alternative to RUM, capable of modeling semi-compensatory behavior and compromise effects.
Purpose of the Study:
- Introduce the RRM modeling approach to healthcare decision-making.
- Evaluate the utility and applicability of RRM in health economics.
- Compare RRM with RUM and Hybrid RUM-RRM models in healthcare contexts.
Main Methods:
- Empirical comparison of RRM, RUM, and Hybrid RUM-RRM models.
- Utilized DCE data on osteoporosis drug treatments and HPV vaccinations.
- Assessed models based on goodness of fit, parameter ratios, and predicted choice probabilities.
Main Results:
- RRM did not significantly outperform RUM for osteoporosis DCE data (p=0.21).
- Hybrid RUM-RRM significantly outperformed RUM for HPV DCE data (p<0.05).
- Predicted choice probabilities showed minor differences; attribute trade-offs varied substantially between models.
Conclusions:
- Model fit differences between RUM, RRM, and Hybrid RUM-RRM were minimal.
- RRM and Hybrid RUM-RRM models demonstrated considerable differences in implied attribute trade-offs.
- RRM and Hybrid RUM-RRM hold potential for novel, policy-relevant health research insights.
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:23Dosage Regimen Designs: Nomograms and Tabulations
Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:15Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...