Generalized Additive Models for the Analysis of EQ-5D Utility Data
Bibliographic record
Abstract
BACKGROUND: Measured utility data have a discrete distribution, and the discreteness is particularly pronounced for EQ-5D utilities. Given the discreteness of the data, modeling the distribution parametrically is likely to be difficult. Moreover, since the distribution is bounded, the linearity assumptions made by many models are questionable. This article suggests using semi-parametric models and illustrates the use of generalized additive models (GAMs) for handling nonlinear associations. METHODS: A simulation study is used to explore whether bias arises when applying parametric models to discrete utility data. A further simulation investigates the bias in semi-parametric linear and quasi-beta regression models when the assumed linearity does not hold and also investigates the use of GAMs. The use of GAMs in practice is shown through a recent study of health utilities among patients with diabetes. RESULTS: Using parametric beta models to analyze discrete EQ-5D utility data led to substantial bias. Both semi-parametric linear regression and quasi-beta regression led to biased estimates of marginal and incremental effects when the mean model was misspecified. The use of GAMs reduced these biases. CONCLUSIONS: Parametric models for EQ-5D utility data should be used with caution. Semi-parametric modeling of utility data should check for nonlinearity. GAMs can help in diagnosing and accommodating nonlinearity.
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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 | 0.036 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".