An International Cross-cohort Harmonization and Data Integration Initiative towards Achieving Statistical Power and Meaningful Results
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Notice bibliographique
Résumé
ABSTRACT
 ObjectivesThe complex interaction between lifestyle, behaviours, genetic factors and the social and physical environment have a fundamental role in modulating risk and/ or progression of health outcomes, especially cancer. To address this complexity, access to large-scale cohorts involving hundreds of thousands of participants and collecting comprehensive and valuable information are required. In the real world however, attaining adequate statistical power presents a major challenge.
 Retrospective data harmonization and integration across multiple cohort studies has been shown to be an effective analytical approach to attaining statistical power, with the potential to support population health research and policy related questions and improve our understanding of the complex factors affecting health outcomes.
 ApproachLarge cohorts, with at least 50,000 participants, initiated in countries all over the world, focused on innovative research on cancer and other chronic diseases were invited to participate in this retrospective data harmonization initiative. Cohorts shared their comprehensive metadata related to their study content and design. Almost 150 variables, selected for their relevance to be part of a generic set of information useful for a broad range of research question, were assessed for their harmonization potential and made available on an online searchable study catalogue. Lastly, a proof of concept research question on the retrospective harmonized data was conducted and aimed to investigate methods to analyze individual patient data from multiple studies by studying the determinants associated with age at menopause.
 ResultsEight cohorts from multiple countries shared their comprehensive metadata related to their study content and design, resulting in over 2 million study participants. Of the 150 potential variables, the majority of them were harmonizable for co-analysis. The proof of concept research question, applied to these variables generated interesting results, widely supported by other research on this topic, found in the literature. This work demonstrates the value of retrospective data harmonization and integration to be an effective analytical approach to attaining statistical power.
 The searchable study catalogue, available online for researchers to use in their own international research projects offers a new innovative tool for potential co-analysis of similar measures collected by separate cohort studies.
 ConclusionRetrospective harmonization offers an innovative approach to optimize use of existing research data with increased statistical power.
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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 | 0,005 | 0,007 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,017 |
| Science ouverte | 0,003 | 0,002 |
| 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