Incomplete information and irrelevant attributes in stated‐preference values for health interventions
Bibliographic record
Abstract
Violations of the assumptions of complete information [CI] and independence of irrelevant alternatives (IIA) in discrete-choice experiment (DCE) data imply sensitivity of preference estimates to the decision context and the alternatives evaluated. There is a paucity of evidence on how these two assumptions affect health-preference results and whether the usual specifications of random-parameters logit models are sufficient to address these violations. We assessed the appropriateness of these assumptions in a DCE valuating interventions to prevent long-term health problems that could be identified through whole genome sequencing. A DCE survey was administered to members of a nationally representative consumer panel to elicit their preferences for options to reduce the risk of health problems. The treatment options presented (surgery, medication, and watchful waiting) and the context for the decisions elicited (severity and likelihood of the health problem) were varied experimentally to evaluate the sensitivity of preference results to such changes. We find evidence of IIA violations as the options presented to prevent health changed. Our results also are consistent with the expectation that additional substitutes decrease the monetized value of alternatives. We also find some evidence that the decision context can moderate such effects, which constitutes a new finding.
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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 | 0.130 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".