Comparing Outcomes of a Discrete Choice Experiment and Case 2 Best-Worst Scaling: An Application to Neuromuscular Disease Treatment
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Case 2 best-worst scaling (BWS-2) is an increasingly popular method to elicit patient preferences. Because BWS-2 potentially has a lower cognitive burden compared with discrete choice experiments, the aim of this study was to compare treatment preference weights and relative importance scores. METHODS: Patients with neuromuscular diseases completed an online survey at two different moments in time, completing one method per occasion. Patients were randomly assigned to either first a discrete choice experiment or BWS-2. Attributes included: muscle strength, energy endurance, balance, cognition, chance of blurry vision, and chance of liver damage. Multinomial logit was used to calculate overall relative importance scores and latent class logit was used to estimate heterogeneous preference weights and to calculate the relative importance scores of the attributes for each latent class. RESULTS: A total of 140 patients were included for analyses. Overall relative importance scores showed differences in attribute importance rankings between a discrete choice experiment and BWS-2. Latent class analyses indicated three latent classes for both methods, with a specific class in both the discrete choice experiment and BWS-2 in which (avoiding) liver damage was the most important attribute. Ex-post analyses showed that classes differed in sex, age, level of education, and disease status. The discrete choice experiment was easier to understand compared with BWS-2. CONCLUSIONS: This study showed that using a discrete choice experiment and BWS-2 leads to different outcomes, both in preference weights as well as in relative importance scores, which might have been caused by the different framing of risks in BWS-2. However, a latent class analysis revealed similar latent classes between methods. Careful consideration about method selection is required, while keeping the specific decision context in mind and pilot testing the methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".