Utility score mapping from FACT-G to EQ-5D-5L and SF-6Dv2 in breast and colorectal cancer patients: a focus on beta mixture models
Bibliographic record
Abstract
OBJECTIVE: To develop algorithms mapping the Functional Assessment of Cancer Therapy-General Scale (FACT-G) onto the EuroQol 5-Dimension 5-level (EQ-5D-5 L) and the Short-Form Six-Dimension version 2 (SF-6Dv2) for patients with breast or colorectal cancers. METHODS: An online survey was conducted to collect responses to FACT-G, EQ-5D-5L, and SF-6Dv2 from cancer patients in Quebec, Canada (N = 202). Linear models including ordinary least squares (OLS), Censored Least Absolute Deviations (CLAD), the robust MM-estimator model (MM), as well as mixture models including two-part model (TPM), and beta-based mixture (betamix) model were used. Mean absolute error (MAE), root mean squared error (RMSE), R2, Bayesian information criteria (BIC), and limits of agreement (LOA) calculated using the five cross-validation to assess the predictive ability of the models. Furthermore, the distribution of observed versus predicted values was assessed using Bland-Altman plot. RESULTS: Based on RMSE and MAE, mixture models better performed than linear models. The betamix model with truncation that included domains and squared terms was the best-performing algorithm for EQ-5D-5 L (MAE = 0.0518, RMSE = 0.0744, R2 = 46.40%, and LOA=-0.166 to 0.165) and SF-6Dv2 (MAE = 0.1375, RMSE = 0.1764, R2 = 35.32%, and LOA=-0.356 to 0.337). EQ-5D-5 L and SF-6Dv2 utility scores for better health states and more severe health states were underestimated and overestimated, respectively. CONCLUSION: This study developed robust algorithms to estimate EQ-5D-5 L and SF-6Dv2 utilities from FACT-G. Consistent with the recent literature, the betamix model outperformed all other econometric models considered in this study. This suggests that mixture models generally exhibit higher performance and are the best choice for mapping.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".