An International Cross-cohort Harmonization and Data Integration Initiative towards Achieving Statistical Power and Meaningful Results
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.610 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.012 | 0.044 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".