Response‐dependent two‐phase sampling designs for biomarker studies
Bibliographic record
Abstract
Abstract Two‐phase sampling designs are developed and investigated for use in the context of a rheumatology study where interest lies in the association between a biomarker with an expensive assay and disease progression. We derive optimal phase‐II stratum‐specific sampling probabilities for analyses from parametric maximum likelihood (ML), mean score (MS), inverse probability weighted (IPW) and augmented inverse probability weighted estimating equations (AIPW). The easy‐to‐implement optimally efficient design for the MS estimator is found to be asymptotically optimal for the IPW and AIPW estimators we consider, and is shown to result in efficiency gains over balanced and simple random sampling even when analyses are likelihood‐based. We further demonstrate the robustness of this optimal design and show that it results in very efficient estimation even when the model or parameters used in its derivation are misspecified. The Canadian Journal of Statistics 42: 268–284; 2014 © 2014 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 | 0.079 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".