The Role of Oral Health in Head and Neck Cancers: Addressing Confounding through Negative Control and Quantitative Bias Analyses
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,312 | 0,430 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,009 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».