Correcting covariate‐dependent measurement error with non‐zero mean
Why this work is in the frame
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Bibliographic record
Abstract
There are many settings in which the distribution of error in a mismeasured covariate varies with the value of another covariate. Take, for example, the case of HIV phylogenetic cluster size, large values of which are an indication of rapid HIV transmission. Researchers wish to find behavioral correlates of HIV phylogenetic cluster size; however, the distribution of its measurement error depends on the correctly measured variable, HIV status, and does not have a mean of zero. Further, it is not feasible to obtain validation data or repeated measurements. We propose an extension of simulation-extrapolation, an estimation technique for bias reduction in the presence of measurement error that does not require validation data and can accommodate errors whose distribution depends on other, error-free covariates. The proposed extension performs well in simulation, typically exhibiting less bias and variability than either regression calibration or multiple imputation for measurement error. We apply the proposed method to data from the province of Quebec in Canada to examine the association between HIV phylogenetic cluster size and the number of reported sex partners. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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