Oral Health—Head and Neck Cancers: Addressing Confounding Through Negative Control and Quantitative Bias Analyses
Bibliographic record
Abstract
OBJECTIVES: While there are plausible biological explanations for the association between oral health and head and neck cancers (HNC), existing studies have yielded conflicting results. A key concern is that these associations are influenced by mediators, unmeasured risk factors, and biases. To address this, a negative control exposure was used to evaluate whether the associations between oral health and HNC risk could be attributed to unmeasured confounding. Additionally, quantitative bias analysis (QBA) was performed to estimate the extent of non-differential misclassification of exposure. METHODS: The HeNCe study, a hospital-based case-control study, recruited incident HNC cases (n = 389) frequency matched to controls (n = 429) by sex and age (within 5 years) from four major referral hospitals in Montreal, Canada. In-person interviews collected information on life course exposures. Unconditional logistic regression estimated the odds ratios (OR) and 95% confidence intervals (CI) for the associations between oral health indicators and HNC, controlling for confounders identified using directed acyclic graphs (DAG). Sexually transmitted diseases (STD) were used as a negative control exposure to test for unmeasured confounding in the associations. QBA, using predetermined bias parameters from previous studies, estimated the magnitude and direction of exposure misclassification bias. RESULTS: Complete denture use and having more than nine missing teeth were associated with an increased HNC risk [OR = 1.33, 95% CI (0.93-1.90) & OR = 1.31, 95% CI (0.93-1.83)], respectively. Similar results were obtained when stratified by HNC subsite. Negative control analysis yielded a null finding, indicating no significant bias due to unmeasured confounders. Bias-corrected estimates of the association between oral health indicators and HNC risk moved further from the null. CONCLUSION: Negative control exposure analysis indicated that unmeasured confounding did not affect the association between oral health and HNC risk. QBA yielded corrected estimates of increased magnitude, suggesting that the crude associations may have been underestimated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".