Bayesian correction for measurement error following group-based exposure assessment in a case-referent study
Bibliographic record
Abstract
<h3>Objectives</h3> We applied Bayesian analysis to case-referent data on occupational noise exposure and death from ischaemic heart disease (IHD) and contrast analyses with and without correction for measurement error in group-level noise exposure estimates. <h3>Methods</h3> A 1:1 matched case-referent study nested in an industrial cohort in England resulted in 117 matched sets; 7225 area noise measurements in dBA from 215 buildings were the basis of modeling building-specific average exposures during the decade of in service IHD death. An additive quasi-Berkson error model was assumed. Bayesian analysis was conducted under varying assumptions about magnitude of error (with SD of error (SDe) up to 10 dBA) and a prior strength of hypothesis (flat vs informative -- 98% range (1.00,1.02) -- prior on OR). All analyses ignored matching and were conducted without adjustment for confounders to estimate log(OR)/dBA in a logistic disease model. <h3>Results</h3> Analysis not corrected for measurement error with flat prior yielded OR 0.99 (95% CrI 0.96–1.02). With flat prior on OR with measurement error correction, OR had 95% CrI 0.97–1.02; with informative prior on the association, the corresponding OR is 1.01, 95% CrI 1.00–1.02, same as prior. The posterior distribution of SDe had median 1.6 (95% CrI 1.2–2.0) dBA. <h3>Conclusions</h3> Measurement error did not bias the uncorrected results. Conditional logistic regression with adjustment for confounders is congruent with Bayesian analysis (115 pairs, OR 0.98, 95% CI 0.94 to 1.02). Analysis provided insights into plausible magnitudes of measurement error in the study. Extension of this methodology to consider matching, confounders and retrospective nature of data is required.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 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 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".