Familial Relative Risk Estimates for Use in Epidemiologic Analyses
Bibliographic record
Abstract
Commonly used crude measures of disease risk or relative risk in a family, such as the presence/absence of disease or the number of affected relatives, do not take into account family structures and ages at disease occurrence. The Family History Score incorporates these factors and has been used widely in epidemiology. However, the Family History Score is not an estimate of familial relative risk; rather, it corresponds to a measure of statistical significance against a null hypothesis that the family's disease risk is equal to that expected from reference rates. In this paper, the authors consider an estimate of familial relative risk using the empirical Bayes framework. The approach uses a two-level hierarchical model in which the first level models familial relative risk and the second considers a Poisson count of the number of affected relatives given the familial relative risk from the first level. The authors illustrate the utility of this methodology in a large, population-based case-control study of breast cancer, showing that, compared with commonly used summaries of family history including the Family History Score, the new estimates are more strongly associated with case-control status and more clearly detect effect modification of an environmental risk factor by familial relative risk.
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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 | 0.061 | 0.345 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".