Attenuation in Risk Estimates in Logistic and Cox Proportional-Hazards Models due to Group-Based Exposure Assessment Strategy
Bibliographic record
Abstract
In occupational epidemiology, it is often possible to obtain repeated measurements of exposure from a sample of subjects (workers) who belong to exposure groups associated with different levels of exposure. Average exposures from a sample of workers can be assigned to all members of that group including those who are not sampled, leading to a group-based exposure assessment. We discuss how this group-based exposure assessment leads to approximate Berkson error model when the number of subjects with exposure measurements in each group is large, and how the error variance approximates the between-worker variability. Under the normality assumption of exposures and with moderately large number of workers in each group, there is attenuation in the estimate of the association parameter, the magnitude of which depends on the sizes of the between-worker variability and the true association parameter. Approximate equations for attenuation have been derived in logistic and Cox proportional-hazards models. These equations show that the attenuation in Cox proportional-hazards models is generally more severe than in logistic regression. Furthermore, when the between-worker variability is large, our simulation study found that the approximation by equation is poor for the Cox proportional-hazards model. If the number of subjects is small, the approximation does not hold for either model.
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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 | 0.121 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".