Quantitative Bias Analysis of misclassification in case-control studies: an example with Human Papillomavirus and Oropharyngeal Cancer.
Bibliographic record
Abstract
OBJECTIVE: Laprise et al. (2019) observed a positive association between oral sex practices and oropharyngeal cancers (OPC) among HPV-negative individuals. Because oral HPV infections are likely to be transmitted through oral sex, these results are counterintuitive. We revisit Laprise et. al's analysis with the objective of estimating the impact of misclassification of HPV infection on the association between oral sex practices and OPC. METHODS: Data were drawn from the Head and Neck Cancer (HeNCe) Life study, a hospital-based case control study of head and neck cancer with frequency-matched controls by age and sex from 4 major referral hospitals in Montreal, Canada. We included only OPC cases (n = 188) and controls (n = 429) and used predictive value weighting, under differential and non-differential scenarios, to evaluate the misclassification. Subsequently, we used logistic regression and 95% confidence intervals to estimate the association between oral sex practice and OPC among HPV-negative individuals. RESULTS: Our results showed that the previously reported association between oral sex practices and OPC among HPV-negative individuals was attenuated or nullified both under differential and non-differential scenarios. CONCLUSION: The association between oral sex practice and OPC could be explained by biases in the data (e.g., HPV mediator misclassification). Our results highlight the need for widespread adoption of Quantitative Bias Analysis in oral health research.
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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 | 0.421 | 0.655 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".