Deriving Health Utility Values From a Health-Related Quality of Life Instrument in Non-Hodgkin Lymphoma Patients
Notice bibliographique
Résumé
Abstract Abstract 2065 Health-related quality of life (HRQOL) is an important measure of health outcome in patients with non-Hodgkin lymphoma (NHL). Cost-effectiveness analyses incorporate health utility, a preference-based summary measure of HRQOL, through the use of quality-adjusted life years (QALYs). However, health utility scores are elicited using generic HRQOL instruments that are not commonly employed, since cancer specific HRQOL instruments generally provide more clinically relevant information. The EORTC QLQ-C30 is a multidimensional instrument designed to evaluate HRQOL in cancer patients, which is summarized using 15 separate scores. The EQ-5D tool, used to determine health utility values, asks patients to rate five domains of health on three different levels. Two hundred forty-three different health states are represented with this instrument, and each health state is converted into a single utility value on a scale anchored at 0 (representing death) and 1 (representing full health), based on societal valuations. The purpose of this study was to develop an algorithm to convert cancer specific HRQOL data obtained from the QLQ-C30 instrument in non-Hodgkin lymphoma (NHL) patients into health utility values elicited from the EQ-5D. NHL patients undergoing chemotherapy at Sunnybrook Health Sciences Centre in Ontario, Canada completed both the QLQ-C30 and EQ-5D questionnaires on each day they attended the clinic to receive a chemotherapy cycle. Fifteen summary scores were calculated from the QLQ-C30 instrument, and health utility values were assigned. A linear regression model was used to quantify the relationship between EQ-5D utility scores and QLQ-C30 summary scores. The QLQ-C30 summary scores were the predictor variables in the model and the utility score was the dependent variable. The final model was established using backward variable elimination with the Akaike Information Criterion (AIC). The predictive ability of the final model was tested using 10-fold cross validation, a technique in which data are divided into ten equal samples and each sample is used once to validate the model while the remaining nine samples are used to fit the model. Fifty-three patients participated in this study, and provided a total of 269 completed QLQ-C30 and EQ-5D questionnaires that were included in the analysis. The mean age of study patients was 61.4 years and 58% were female. 77% of patients had a diagnosis of the diffuse large B-cell subtype of NHL. The final model included four QLQ-C30 summary scores: physical (p<0.001), emotional (p<0.001), cognitive (p=0.06), and pain (p<0.001). Table 1 summarizes actual and predicted utility scores. Predicted utility scores were based on the results of the cross validation. The mean absolute error between predicted and actual utility scores was 0.07.Table 1:Summary measures for actual and predicted health utility scoresStatisticActualPredictedDifference*Mean ± standard deviation0.84 ± 0.160.84 ± 0.130.00 ± 0.09Median0.830.870.0195% confidence interval for the mean (based on 3000-replicate bootstrap)[0.82, 0.86][0.83, 0.86][−0.01, 0.01]*Predicted minus Actual, by patient This analysis demonstrates that HRQOL data collected from NHL patients using the EORTC QLQ-C30, a multidimensional, non-preference-based instrument, can be converted into preference-based data suitable for use in cost-effectiveness analyses. Disclosures: Lathia: Amgen Canada: Research Funding. Mittmann:Amgen Canada: Research Funding.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».