Adaptive response‐dependent two‐phase designs: Some results on robustness and efficiency
Bibliographic record
Abstract
Large cohort studies now routinely involve biobanks in which biospecimens are stored for use in future biomarker studies. In such settings, two-phase response-dependent sampling designs involve subsampling individuals in the cohort, assaying their biospecimen to measure an expensive biomarker, and using this data to estimate key parameters of interest under budgetary constraints. When analyses are based on inverse probability weighted estimating functions, recent work has described adaptive two-phase designs in which a preliminary phase of subsampling based on a standard design facilitates approximation of an optimal selection model for a second subsampling phase. In this article, we refine the definition of an optimal subsampling scheme within the framework of adaptive two-phase designs, describe how adaptive two-phase designs can be used when analyses are based on likelihood or conditional likelihood, and consider the setting of a continuous biomarker where the nuisance covariate distribution is estimated nonparametrically at the design stage and analysis stage as required; efficiency and robustness issues are investigated. We also explore these methods for the surrogate variable problem and describe a generalization to accommodate multiple stages of phase II subsampling. A study involving individuals with psoriatic arthritis is considered for illustration, where the aim is to assess the association between the biomarker MMP-3 and the development of joint damage.
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How this classification was reachedexpand
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 | 0.018 | 0.209 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".