Framework for Exploration of Statistical Heterogeneity in Multi-Database Studies: A Case Study Using EXACOS-CV Studies
Notice bibliographique
Résumé
Purpose: Multi-database studies may provide heterogeneous results despite using common protocols, leading to challenges in interpretation, but also providing an opportunity to gain insights on populations or healthcare systems. The objectives of these analyses were to develop a framework for exploring sources of statistical heterogeneity and apply it to the multi-database EXACOS-CV (EXAcerbations of COPD and their OutcomeS on CardioVascular diseases) program. Methods: A conceptual framework to systematically assess sources of statistical heterogeneity in multi-database studies was developed. This framework distinguishes between methodological diversity and true clinical variation. Methodological diversity includes differences in study design and database selection, while true variation considers population and healthcare differences. Possible sources of methodological diversity were identified via a novel checklist and explored. In turn, hypotheses were generated about true variation. The framework and checklist were applied to EXACOS-CV cohort studies in Germany, Canada, the Netherlands, and Spain which deviated least from the common protocol and so were included. Focus was on adjusted hazard ratios (aHR) for post-exacerbation associations with decompensated heart failure (HF) and all-cause death, for which results were most and least heterogeneous, respectively. Results: Across EXACOS-CV studies, the adjusted hazard ratios (aHR) for HF in the first 1-7 days post-exacerbation, compared to non-exacerbation periods, ranged from 2.6 (95% CI, 2.3, 2.9) in Germany to 72.3 (64.4, 81.2) in Canada, and the association with death, relative to non-exacerbation periods, ranged from 3.5 (2.4, 5.3) in the Netherlands to 22.1 (19.9, 24.4) in Spain. Completed methodological diversity checklists linked differences in aHRs to possible variation in ability to capture pre-existing cardiovascular comorbidities across studies, as well as differences in confounder measurement. Standardizing adjusted models across studies did not fully explain heterogeneity, suggesting other contributing factors. Heterogeneity may result from genuine variation in prevalence of CV disease. It was hypothesized that patients with pre-existing CV disease have more accurate diagnoses and management of post-exacerbation CV events, possibly leading to lower risks of such events. Conclusion: Multi-database studies can provide directional insights on the study research question while considering healthcare system and population differences. The developed framework aids assessment of heterogeneity sources.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 0,166 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».