8277626 Application of bias analysis: the case of socioeconomic status and lung cancer in a pooled international case-control study
Bibliographic record
Abstract
Objective Socioeconomic status (SES) is an important confounder of the association between occupational exposures and lung cancer. However, the extent of SES effects on lung cancer, including mediating effects via smoking habits, remained unclear as several biases were often indicated. Therefore, we aimed to quantify the impact of potential biases on the SES-lung cancer association. Material and Methods We used data from the SYNERGY project (https://synergy.iarc.who.int), including 16,550 cases and 20,147 controls from European and Canadian case-control studies, to investigate bias effects in the association of occupational SES (in quartiles) and lung cancer. We estimated odds ratios (OR) with 95% confidence intervals (CI) by logistic regression adjusting for age, study centre, and smoking, stratified by sex. We also estimated natural direct SES effects and natural indirect effects via smoking by inverse odds ratio weighting. In a quantitative bias analysis, we considered impacts of misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic disposition, calculating 95% simulation intervals (SI) by bootstrap (n=2500 repetitions). Results Adjustment for smoking as well as natural effects estimation showed that nearly 50% of lung cancer risks for lower SES groups in men and up to one third in women were attributable to smoking. Consideration of all types of bias reduced lung-cancer risks in the fully adjusted logistic regression models: For the 4th versus 1st (highest) SES quartile OR decreased from 1.83 (1.69-1.98 CI) to 1.50 (1.32-1.69 SI) in men, and OR 1.48 (1.27-1.72 CI) to 1.20 (1.01-1.45 SI) in women. Conclusion Direct lung-cancer risks of lower SES groups were lowered by multiple bias adjustment but remained elevated. As long as it remains unclear to what extent these effects are attributable to occupational hazards, SES should be considered in the analysis of occupational exposures and lung cancer.
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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 | 0.087 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".