Illustration of the Impact of Unmeasured Confounding Within an Economic Evaluation Based on Nonrandomized Data
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Notice bibliographique
Résumé
Background: Propensity score (PS) methods are frequently used within economic evaluations based on nonrandomized data to adjust for measured confounders, but many researchers omit the fact that they cannot adjust for unmeasured confounders. Objective: To illustrate how confounding due to unmeasured confounders can bias an economic evaluation despite PS matching. Methods: We used data from a previously published nonrandomized study to select a prematched population consisting of 121 patients (46.5%) who received endovascular aneurysm repair (EVAR) and 139 patients (53.5%) who received open surgical repair (OSR), in which sufficient data regarding eight measured confounders were available. One-to-one PS matching was used within this population to select two PS-matched subpopulations. The Matched PS-Smoking Excluded Subpopulation was selected by matching patients using a PS model that omitted patients’ smoking status (one of the measured confounders), whereas the Matched PS-Smoking Included Subpopulation was selected by matching patients using a PS model that included all eight measured confounders. Incremental cost-effectiveness ratios (ICERs) were assessed within both subpopulations. Results: Both subpopulations were composed of two different sets of 164 patients. Balance within the Matched PS-Smoking Excluded Subpopulation was achieved on all confounders except for patients’ smoking status, whereas balance within the Matched PS-Smoking Included Subpopulation was achieved on all confounders. Results indicated that the ICER of EVAR over OSR differed between both subpopulations; the ICER was estimated at $157,909 per life-year gained (LYG) within the Matched PS-Smoking Excluded Subpopulation, while it was estimated at $235,074 per LYG within the Matched PS-Smoking Included Subpopulation. Discussion: Although effective in controlling for measured confounding, PS matching may not adjust for unmeasured confounders that may bias the results of an economic evaluation based on nonrandomized data.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,040 | 0,166 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle