The potential value of sibling controls compared with population controls for association studies of lifestyle-related risk factors: an example from the Breast Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: A previous Australian population-based breast cancer case-control study found indirect evidence that control participation, although high, was not random. We hypothesized that unaffected sisters may provide a more appropriate comparison group than unrelated population controls. METHODS: Three population-based case-control-family studies of breast cancer in women of white European origin were carried out by the Australian, Ontario and Northern California sites of the Breast Cancer Family Registry. We compared risk factors between 3643 cases, 2444 of their unaffected sisters and 2877 population controls and conducted separate case-control analyses based on population and sister controls using unconditional multivariable logistic regression. RESULTS: Compared with sister controls, population controls were more highly educated, had an earlier age at menarche, fewer births, their first birth at a later age and their last birth more recently. The established breast cancer associations detected using sister controls, but not detected using population controls, were decreasing risk with each of later age at menarche, more births, younger age at first birth and greater time since last birth. CONCLUSIONS: Since participation of population controls might be unintentionally related to some risk factors, we hypothesize that sister controls could provide more valid relative risk estimates and be recruited at lower cost. Given declining study participation by population controls, this contention is highly relevant to epidemiologic research.
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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 | 0.089 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".