Selection models for efficient two‐phase design of family studies
Why this work is in the frame
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Bibliographic record
Abstract
Family studies routinely employ biased sampling schemes in which individuals are randomly chosen from a disease registry and genetic and phenotypic data are obtained from their consenting relatives. We view this as a two-phase study and propose the use of an efficient selection model for the recruitment of families to form a phase II sample subject to budgetary constraints. Simple random sampling, balanced sampling and use of an approximately optimal selection model are considered where the latter is chosen to minimize the variance of parameters of interest. We consider the setting where family members provide current status data with respect to the disease and use copula models to address within-family dependence. The efficiency gains from the use of an optimal selection model over simple random sampling and balanced sampling schemes are investigated as is the robustness of optimal sampling to model misspecification. An application to a family study on psoriatic arthritis is given for illustration.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it