Purpose: We apply a newly developed preference measurement method, Adaptive Best-worst Conjoint (ABC), to improve questioning efficiency for patient treatment preferences. With this approach, utility functions with 7-10 parameters are estimated instantaneously at the individual level with as few as 12-15 tasks completed in 10 minutes.
Method: Conjoint analysis respondents choose the best and worst of four treatment alternatives (attribute bundles). The method adaptively presents the next four treatment alternatives, and after 12-15 tasks a utility function is estimated and an individualized report is printed. This report summarizes the patient's treatment preferences and priorities, and serves to enhance the doctor-patient discussion of possible treatments. Adaptive Best-worst Conjoint with four options-at-a-time identifies five of the six possible paired comparisons (Best > option B, Best > option C, Best > Worst, option B > Worst, option C > Worst; only B is not compared to C). So ABC is 66% more efficient than traditional choice-based conjoint even without adaptive questioning. This inherent efficiency advantage of best-worst questioning is further enhanced through adaptive questioning based on transitivity of preference. That is, we assume that if full-profile A is preferred to full-profile B, and if B > E, then A is also > E, even though we never directly compared A to E. Such transitivity may resolve even more paired comparisons than direct questioning. For example, with 16-full-profiles, there are 16 x 15 / 2 = 120 possible paired comparisons, over 50% of which are resolved through transitivity.
Results: The presentation highlights three key results: •Internal consistency: The estimated utility functions explain 88%, on average, of the variance in treatment scores. •Estimation and prediction: a comparison of linear regression, LINMAP, and Hierarchical Bayes shows that the abc method has high predictive accuracy (e.g., 68% first choice-out-of-four hit rate for holdout questions). Also, 80%-90% of paired comparisons in holdouts are consistent with the estimated utility function. •Respondent and physician reactionsto this system have been favorable as compared with a control group.
Conclusion: Adaptive Best-worst Conjoint analysis compares favorably with Ratings Scale and Time Tradeoff as a way of measuring patient preferences. This presentation will lay out the Excel-based method as a direct takeaway from the SMDM conference.
See more of: The 34th Annual Meeting of the Society for Medical Decision Making