Statistical challenges in high‐dimensional molecular and genetic epidemiology
Bibliographic record
Abstract
Abstract Molecular and genetic association studies conducted in well‐characterized longitudinal cohorts offer a powerful approach to investigate factors influencing disease course or complex trait expression. As measurement technologies continue to develop and evolve, studies based on existing cohorts raise methodological challenges. Five such challenges are illustrated in two long‐term inter‐disciplinary collaborations. In one, molecular genetic prognostic factors in the natural history of node‐negative breast cancer are investigated using a combination of hypothesis‐testing and hypothesis‐generating molecular approaches. In the other, genome‐wide association methods are applied to identify genes for multiple traits in extended follow‐up data from participants of a therapeutic RCT in type 1 diabetes. The Canadian Journal of Statistics 46: 24–40; 2018 © 2017 Statistical Society of Canada
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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 | 0.382 | 0.653 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".