Accounting for Preference Heterogeneity in Discrete Choice Experiments Using Hierarchical Bayes
Notice bibliographique
Résumé
The econometric modelling of discrete choice experiments (DCEs) in health economics has recently incorporated preference heterogeneity using the mixed logit (MXL) specification. Modelling heterogeneity combines estimates of the population distribution with information from individuals' choices. There are, however, some known difficulties with the estimation of the MXL model if a frequentist approach is taken. Namely, convergence to the maximum of the simulated likelihood (MSL) function can be difficult if the starting values are not close to their maximum, or if certain distributions are specified. This is because the likelihood function may have multiple local maxima, or not be well approximated by a quadratic. An alternative strategy for estimating the MXL model is to employ a hierarchical Bayes (HB) approach. HB shares the same behavioural assumptions of the frequentist MXL approach, but differs in its estimation procedure and interpretive philosophy. HB uses Gibbs sampling and Metropolis-Hastings to determine the joint posterior. HB does not require the maximization of a likelihood function, thus avoiding issues of local versus global maxima. Despite their differences, HB and MSL may produce the same numerical results. The Bernstein-von Mises theorem states that if the mean of the posterior is taken as an estimate, it will converge asymptotically to the maximum likelihood estimator. This result may not hold in small samples because the approaches differ in their treatment of uncertainty. The objective of this study was to investigate the specification, estimation and performance of the HB approach using data collected from a DCE researching preferences for a novel technology identifying genetic causes of developmental delay. 756 respondents recruited using a market research company completed 16 choice questions, each with three alternatives. The first two alternatives differed on the bases of three attributes: likelihood of genetic diagnosis, time waiting for results, and cost. The third alternative is an opt-out option to model non-demanders. The advantages and comparability of the HB and MSL approaches are also investigated by applying the same random parameter structure using normal and log normal distributions; both models also accounted for the panel structure of the data. Given that the scale parameter may confound a direct comparison of the parameters, the estimates from the frequentist MXL will be rescaled such that the cost coefficient is normalized to be the same for the two procedures. We also test the forecasting ability of either approach using the expected coefficients in the logit formula to calculate the probability of a respondent choosing an alternative. If a model specification is accurate, the alternative with the highest probability in a given choice set should be chosen in the majority of circumstances. The HB and MSL models yielded similar parameter estimates and were equally successful in predicting within-sample responses. In this particular analysis, the HB approach resulted in a quicker estimation time; however, this result may not hold if other distributions are specified. This is because drawing from the conditional posterior becomes complicated if non-normal distributions are specified.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,094 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».