5N-5 EMPIRICAL COMPARISON OF STATED AND REVEALED PREFERENCES USING BAYESIAN MIXED LOGIT ESTIMATION FROM A DISCRETE CHOICE EXPERIMENT: AN APPLICATION TO LATENT TUBERCULOSIS INFECTION TREATMENT

Wednesday, October 26, 2016: 11:00 AM
Bayshore Ballroom Salon E, Lobby Level (Westin Bayshore Vancouver)

Tima Mohammadi, MSc, MA, Centre for Health Evaluation and Outcome Sciences, University of British Columbia, Vancouver, BC, Canada, Nick Bansback, PhD, University of British Columbia; Centre for Clinical Epidemiology and Evaluation; Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada, Fawziah Lalji, PHARMD, FCSHP, Faculty of Pharmaceutical Sciences,‎University of British Columbia, Vancouver, BC, Canada, Amir Khakban, MSc, Collaboration for Outcomes Research and Evaluation, University of British Columbia, Vancouver, BC, Canada, Jonathon Campbell, Faculty of Pharmaceutical Sciences, Vancouver, BC, Canada, J Mark FitzGerald, MD, FACP., UBC , Department of Medicine,The Lung Centre, Vancouver, BC, Canada, Larry D. Lynd, PhD, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada and Carlo Marra, PhD, Memorial University, St. John's, NF, Canada
Purpose:

    The hypothetical nature of stated preference data raises an important question about its validity in characterizing respondents’ actual behaviour. The objective of this study was to compare the forecasted choices of respondents using stated preferences in a discrete choice experiment (DCE) to their observed actual choices at an individual level.

Method:

    A DCE was performed in patients prior to being offered treatment for latent tuberculosis infection. A mixed logit model was estimated using hierarchical Bayes. The individual-specific preference coefficients were used to calculate the expected probability of choosing the treatment by each patient. The forecasted choice using this probability was compared to their actual decision. We examined the comparability of different distributions for the random parameters. We also explored the predictive power of DCE using different thresholds to convert probabilities into the predicted choices and a Receiver Operating Characteristic (ROC) curve.

Result:

    Our results identified significant heterogeneity in preferences for all attributes among respondents. The model with log-normal distribution for attributes representing treatment side effects improved model fit and predictive power compared to the other model specifications.  The best model correctly predicted actual decisions for 83% of participants. The predictive performance of the DCE results was also confirmed using a threshold that maximizes Youden's index and the ROC curev. The area under the ROC was 0.8237. We also showed that individual-specific coefficients reflected respondents’ actual choices more closely compared to the aggregate level estimates. While the probability of the chosen alternative over the sample was 69% based on the aggregate coefficients, the average of predicted individual probabilities of the chosen alternative was 82%.

Conclusion:

    In summary, our findings showed that DCE as a method of obtaining stated preferences can yield similar results to revealed preferences at the individual level in this setting. However future investigations are required to establish the predictive power of DCEs in different settings.

See more of: ORAL ABSTRACTS: ASSESSING PATIENT PREFERENCES AND PRIORITIES

See more of: 38th Annual North American Meeting of the Society for Medical Decision Making