Abstract 189: Expressing Continuous Cardiovascular Outcome Data in Absolute Terms for use in Patient Treatment Decision Aids: Validation of a Proposed Method
Notice bibliographique
Résumé
Background: Patient decision aids (DA) supplement advice from health care professionals through provision of information on the likelihood of risks and benefits of treatment options. Ideally, these data are expressed in both visual and written form to communicate absolute risk or benefit (i.e., X/100). However, this well-accepted methodology lends itself to outcomes which are binary only. Valid methods for conversion of continuous outcomes for presentation in DAs are not well-established. The difficulty in converting these outcomes is compounded by the need to extract and pool aggregate standardized score data from multiple published sources to generate a single estimate of treatment effect. Our team was met with this challenge when developing a DA for refractory angina; the main outcome was angina frequency (AF) as measured by the AF subscale of the Seattle Angina Questionnaire. We aimed to develop and test the validity of a proposed method, based on statistical theory, for estimating absolute angina reduction based on AF scores. Methods: AF summary statistics, M(SD), were extracted from 2 distinct intervention studies for which raw data were accessible. A clinically important difference in AF scores was identified through expert consultation. Based on normal distribution theory, aggregate data from both studies were used to estimate the proportion of those who experienced a clinically significant change in AF. Chi-square comparisons of proportions was then used to determine if these estimates were dissimilar from true numbers reflected in the raw data. We then generated 500,000 simulated datasets using the same AF summary statistics, but with widely varying distribution characteristics (e.g., positive skew). Multiple comparisons of estimates to actual proportions of those experiencing a clinically significant change in AF were generated under simulated scenarios and expressed graphically. The effect of summary statistic on these comparisons was examined by conducting the simulations using both M(SD) and medians and interquartile ranges (IQR). Results: Overall agreement between estimated and actual proportions of those experiencing a clinically significant change in AF was excellent for the real study data; there were no significant differences (p >0.45), and no difference exceeded 5%. These results remained stable when data were pooled from the 2 studies using meta-analysis. For the simulated data sets, concordance between the estimated and actual proportions was also moderate to good in cases where data were not highly skewed (i.e. skewness <2.5); agreement improved in the context of medians and IQRs. Conclusion: Our results suggest that standard statistical theory can be used to estimate continuous outcomes in absolute terms with reasonable accuracy for use in DAs; caution is advised if outcomes are expected to be highly skewed in distribution.
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Comment cette classification a été obtenuedéplier
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 | 0,022 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».