The Role of Oral Health in Head and Neck Cancers: Addressing Confounding through Negative Control and Quantitative Bias Analyses
Bibliographic record
Abstract
ABSTRACT:Background: In the past two decades, there is a growing interest in the relationship between oral health indicators and head and neck cancers (HNC). Even though there is a strong biological plausibility to support these associations, the literature remains controversial, with some studies reporting strong positive associations, while others report no, or negative associations. Some authors contend that these links are influenced by underlying mediators, unmeasured risk factors, errors, and biases in the data, leading to these discrepancies. The use of negative controls, an epidemiological tool routinely used to detect bias in observationalstudies, offers an opportunity to clarify this issue. Although negative controls help identify and distinguish spurious associations from true ones, they have not been routinely used in epidemiological studies in oral health research.Objective: To estimate the extent to which the association between oral health indicators and HNC risk is due to unmeasured confounders using data from the HeNCe life study. Additionally, we plan to estimate the magnitude of non-differential misclassification bias due to exposure using probabilistic sensitivity analysis (PSA).Methods: The data for this investigation come from the HeNCe life study - Canadian site. This hospital-based case-control study recruited incident cases of HNC (n=389) frequency matched to controls (n=429) by sex and age within five years from four major referral hospitals in Montreal, Canada. In-person interviews using life-grid-based questionnaires collected information on a wide array of life course exposures. Oral rinse and oral brush specimens were analysed for HPV positivity and genotyping. The main exposure variables (oral health indicators) included self-reported number of missing teeth, denture use, and mouthwash use.We estimated odds ratios (OR) and 95% confidence intervals (CI) for the associations between oral health indicators and HNC using unconditional logistic regression models. The negative control exposure, sexually transmitted diseases (STD), was selected based on whether any of the following diseases, syphilis, gonorrhea, chlamydia, and herpes were present or absent. Current literature does not provide substantial evidence connecting these diseases to head and neck cancer (HNC). We used unconditional logistic regression models and OR and 95% CI to estimate the association between STD and HNC risk, assuming a null hypothesis. Probabilistic sensitivity analysis (PSA) was done to estimate the magnitude and direction of misclassification bias. Corrected estimates were obtained by using priors selected from previous validation studies of self-reported oral health measures.Results: The use of complete dentures and having more than 9 missing teeth suggested an increase in 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 detectable presence of bias due to unmeasured confounders. Bias-corrected estimates of the association between oral health indicators and HNC risk for the predicted sensitivity and specificity values showed stronger associations that shifted further from the null.Conclusion: Our findings suggest that the associations between oral health indicators and HNC risk observed in the existing literature are potentially true and are not influenced by unmeasured confounders or biases. PSA findings also yield corrected estimates with stronger magnitude suggesting that the associations were underestimated in the crude analysis
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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 | 0.312 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".