Harmonization of Cognitive Measures in Individual Participant Data and Aggregate Data Meta-Analysis
Bibliographic record
Abstract
Objectives The aim of this study was to identify approaches to statistical harmonization which could be used in the context of summary data and/or individual participant data meta-analysis of cognitive measures and to apply and evaluate these different approaches to cognitive measures from three studies. Data Sources MEDLINE®, Embase, Web of Science and MathSciNet with a supplemental search using the Google search engine. The references of relevant articles were also checked and a search for more recent articles that cited the articles already identified as being of interest was undertaken. Review methods A two-pronged approach was taken for this environmental scan. First, a search of studies that quantitatively combined data on cognition was conducted. The second component was to identify general literature on statistical methods for data harmonization. Standard environmental scan methods were used to conduct these reviews. The search results were rapidly screened to identify articles of relevance to this review. The references of relevant articles were checked and a search for more recent articles that cited the articles already identified as being of interest was undertaken. Results Three general classes of statistical harmonization models were identified: (1) standardization methods (e.g., simple linear-, Z-transformations, T-scores, and C-scores); (2) latent variable models; and (3) multiple imputation models. Cross-sectional data from three studies including 9,269 participants were included in the applied analyses to examine the relationship between physical activity and cognition. A harmonization process was undertaken to determine the combinability of data across studies. The latent variable analysis underscored the difficulty harmonizing these cognition data. In general consistency was found among the statistical harmonization methods; however, there was some evidence that heterogeneity can be masked when specific standardization methods were used. Conclusions This study provides empirical evidence to inform methods of combining complex constructs using aggregate data (AD) or individual participant data meta-analysis. The results underscore that very careful consideration of inferential equivalence needs to be undertaken prior to combining cognition data across studies. Of the three methods of statistical harmonization for cognition data, T-score standardization is the least desirable compared with the centered score method or latent variable methods. Finally, assessment of the assumptions underlying statistical harmonization is not possible without some individual-level data which are required to assess the potential for bias in combining complex outcomes using AD meta-analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".