Commentary. In Praise of Studies That Use More Than One Generic Preference-Based Measure
Bibliographic record
Abstract
OBJECTIVES AND BACKGROUND: Generic preference-based (GPB) measures of health-related quality of life (HRQL) are widely used as outcome measures in cost-effectiveness and cost-utility analyses (CEA, CUA). Health technology assessment agencies favor GPB measures because they facilitate comparisons among conditions and because the scoring functions for these measures are based on community preferences. However, there is no gold standard HRQL measure, scores generated by GPB measures may differ importantly, and changes in scores may fail to detect important changes in HRQL. Therefore, to enhance the accumulation of empirical evidence on how well GPB measures perform, we advocate that investigators routinely use two (or more) GPB measures in each study. METHODS: We discuss key measurement properties and present examples to illustrate differences in responsiveness for several major GPB measures across a wide variety of health contexts. We highlight the contributions of longitudinal head-to-head studies. RESULTS: There is substantial evidence that the performance of GPB measures varies importantly among diseases and health conditions. Scores are often not interchangeable. There are numerous examples of studies in which one GPB measure was responsive while another was not. CONCLUSIONS: Investigators should use two (or more) GPB measures. Study protocols should designate one measure as the primary outcome measure; the other measure(s) would be used in secondary analyses. As evidence accumulates it will better inform the relative strengths and weaknesses of alternative GPB measures in various clinical conditions. This will facilitate the selection and interpretation of GPB measures in future studies.
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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 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.017 | 0.006 |
| Research integrity | 0.104 | 0.090 |
| Insufficient payload (model declined to judge) | 0.022 | 0.026 |
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".