Underestimation of Uncertainties in Health Utilities Derived from Mapping Algorithms Involving Health-Related Quality-of-Life Measures
Bibliographic record
Abstract
OBJECTIVES: Mapping algorithms are being developed in increasing numbers to derive health utilities (HUs) from health-related quality-of-life (HRQOL) data. However, the variances of the mapping-derived HUs are observed to be smaller than those of the actual HUs. METHODS: Two reasons are proposed: 1) the presence of important unmeasured predictors leading to a high degree of unexplained variance and 2) ignoring that the regression coefficients are random variables themselves. We derive 3 variance estimators of HUs to account for these causes: 1) R (2)-adjusted estimator, 2) parametric estimator, and 3) nonparametric estimator. We tested these estimators using a simulated dataset and a real dataset involving the EQ-5D-3L and University of Washington Quality of Life questionnaire for patients with head and neck cancers. RESULTS: The R (2)-adjusted estimator can be used in ordinary least squares (OLS)-based mapping algorithms and requires only the R (2) from the derivation study. The parametric estimator can be used in OLS-based mapping algorithms and requires the mean square error (MSE) and design matrix from the derivation study. The nonparametric estimator can be used in any mapping algorithm and requires leave-one-out cross-validation MSE from the derivation study. In the simulated dataset, all 3 estimators are within 1% of the variance of the actual HUs. In the real dataset, the unadjusted variance was 45% less than the actual variance, while all 3 estimators are within 10% of the actual variance. CONCLUSIONS: When conducting cost-utility analyses (CUA) based on mapping algorithms, the variances of derived HUs should be properly adjusted using one of the proposed methods so that the results of the CUAs will correctly characterize uncertainty.
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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 | 0.050 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".