Covariate Adjusted Confidence Intervals for the Incremental Cost-Effectiveness Ratio in Non Randomized Trails, Comparing Fieller and Bootstrap Methods - An Example from the Sarah Study
Notice bibliographique
Résumé
Background: Assessment of uncertainty in cost-effectiveness analysis is of high importance as it greatly impacts use of cost-effectiveness results in decision making and priority setting. While in recent years various methods have been suggested for the estimation of confidence intervals to the incremental cost-effectiveness ratio (ICER) when groups are randomly assigned to the treatment groups, relatively little work has been done in the case of non-randomized studies. Objective: Aim of this study is to describe a method of resampling based confidence intervals in the context of non-randomized trial requiring covariate adjustment and to compare it to a recently suggested parametric approach. Its suitability and first indication of relative performance are discussed using data from a non randomized trial comparing inpatient and outpatient rehabilitation in patients after an acute myocardial infarction (SARAH-Study). Methods: Covariate adjustment was performed using a seemingly unrelated regression (SURE) approach. Regression equations for costs and effects were jointly estimated allowing to incorporate the correlation between cost and effects. Point estimate of the ICER ware calculated by the quotient of the coefficient of the therapy-dummy in the cost and effect equation. Parametric confidence intervals for the ICER were calculated using the Fieller formula. Non parametric approach was based on bootstrapping the SURE regression model. Resampled point estimates of the ICER were transformed into angular deviations and standard methods for bootstrap confidence intervals were applied. Results: The 163 patients enrolled into the SARAH-trial preferred predominantly the inpatient to the outpatient rehabilitation (112 vs. 51). Based on pre-post rehabilitation data patients in the outpatient group incurred lower gains in quality of life (p=0,094) but caused also considerably lower cost (p0.12 since conclusions of the analytic term became complex with smaller levels of alpha, reflecting the insignificance of the effect estimate. Due to the non-parametric nature of the bootstrap method intervals could be calculated for any confidence level. They also tended to be wider for the same alpha levels compared to Fieller confidence intervals. Conclusion: Assessment of the uncertainty in the ICER is crucial also in a non randomized context. Different methods can be applied for this purpose. The Fieller method which is based on the normality assumptions tends to give exact solution if assumptions are met. In the example however it exhibited its known problem of yielding no solutions for alpha levels of interest if differences in effects and/or costs are too small. The approach of bootstrapping the regression seemed here suitable, although it tended to show wider confidence intervals. This non-parametric approach offered a valuable option in this non randomized trial setting, of which a detailed analysis of performance will be investigated in further simulation analyses.
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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 | 0,192 | 0,390 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».