A novel approach for health state valuation: Multiple bounded dichotomous choice compared to the traditional standard gamble
Bibliographic record
Abstract
OBJECTIVE: to assess the feasibility of a new stated preference approach, the multiple bounded dichotomous choice (MBDC), designed to generate value sets for preference-based measurement of health-related quality of life. METHODS: MBDC and standard gamble (SG) tasks were completed to derive SF-6Dv2 value sets from a sample of the general population in Quebec, Canada. Participants were randomized between the two approaches: 6 health states were evaluated in SG and 11 health states in MBDC. Several models were used to estimate data in each approach, and the preferred models were chosen by using mean absolute error (MAE), logical consistency of parameters, and significance levels. Results of MBDC were compared with SG in terms of acceptability (self-reported difficulty and quality levels in answering, and completion time), consistency (monotonicity of model coefficients), accuracy (standard errors), dimensions coefficient magnitude, correlation between the value sets estimated, and the range of estimated values. The intra-class correlation coefficient (ICC) was computed to assess value sets' consistency. RESULTS: Out of 655 individuals who completed MBDC tasks and 828 who completed SG tasks, a total of 585 participants for MBDC and 714 for SG tasks were included for analysis. The preferred models for both approaches were GLS Tobit. No significant difference was observed in self-reported difficulties and qualities in answers among approaches, but MBDC had less excluded participants and was less prone to report difficulties in answering. Additionally, completion time in the MBDC group was significantly lower (99.80 vs 68.12 s). Most standard errors in the MBDC were lower than those in SG, and the number of non-significant parameters was also lower. The range of utility values generated by MBDC tended to be wider (-0.372 to 1) than those generated by the SG (-0.137 to 1) and the number of worse-than-dead states in MBDC (0.91%) was higher than for SG (0.08%). The Pain dimension was identified as the most significant, while the Vitality dimension showed the lowest significant decrement. Both approaches exhibited a tendency to overestimate severe health state values and underestimate better health state values. The correlation and ICC between the two value sets were 0.937 and 0.983, respectively. CONCLUSION: Based on empirical evidence, it can be inferred that the MBDC method is not only feasible but also holds the potential to generate meaningful and well-informed preference data from respondents. This approach can be used to derive a value set for preference-based instrument.
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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 | 0.039 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".