Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data
Why this work is in the frame
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Bibliographic record
Abstract
The current interest in undertaking cost-effectiveness analyses alongside clinical trials has lead to the increasing availability of patient-level data on both the costs and effectiveness of intervention. In a recent paper, we show how cost-effectiveness analysis can be undertaken in a regression framework. In the current paper we develop a direct regression approach to cost-effectiveness analysis by proposing the use of a system of seemingly unrelated regression equations to provide a more general method for prognostic factor adjustment with emphasis on sub-group analysis. This more general method can be used in either an incremental cost-effectiveness or an incremental net-benefit approach, and does not require that the set of independent variables for costs and effectiveness be the same. Furthermore, the method can exhibit efficiency gains over unrelated ordinary least squares regression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it