Application of two-part models and Cholesky decomposition to incorporate covariate-adjusted utilities in probabilistic cost-effectiveness models
Bibliographic record
Abstract
This study aimed to explain a joint statistical procedure (two-part models and Cholesky decomposition) to incorporate second-order uncertainty from covariate adjusted mean utility functions in probabilistic cost-effectiveness models. First, two-part models were applied to obtain parameters for the utility function. Second, a new set of correlated parameters for each simulation was obtained by Cholesky decomposition. The procedure was applied to EuroQol5D-5L in the Spanish Health Survey (21,007 adults). An example for the first simulation showed that 71% of men aged 60 years, high social status and normal weight were in perfect health, and in those not in perfect health, the expected utility was 0.8474 (= 1 − 0.1526). Therefore, their estimated mean utility value was 0.9559. Mean utility values in the interval (− ∞1] were calculated and their associated uncertainty incorporated in the cost-effectiveness models, based on the uncertainty related to correlated parameters in the utilities function. El objetivo de este estudio fue explicar el procedimiento estadístico conjunto (modelos en dos etapas y descomposición de Cholesky) para incorporar la incertidumbre de segundo orden asociada a la función de utilidad media ajustada por variables en los modelos probabilísticos de coste-efectividad. Primero se aplicaron los modelos en dos etapas para obtener los parámetros de la función de utilidad. Segundo, para cada simulación, se obtuvo un nuevo conjunto de parámetros correlacionados utilizando la descomposición de Cholesky. El procedimiento se aplicó al cuestionario EuroQol5D-5L de la Encuesta Nacional de Salud en España (21.007 adultos). El ejemplo de una primera simulación mostró que el 71% de los hombres de 60 años, con clase social alta y peso normal tenían una salud perfecta, y en aquellos que no tenían una salud perfecta la utilidad esperada fue de 0,8474 (= 1 − 0,1526). Por lo tanto, su utilidad media estimada fue de 0,9559. Se calcularon utilidades medias con valores comprendidos en el intervalo (-∞1] y se incorporó la incertidumbre asociada a ellos en los modelos de coste-efectividad, basándose en la incertidumbre correspondiente a los parámetros correlacionados de la función de utilidad.
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How this classification was reachedexpand
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".