An application of semi-bayes modeling to a study of the occupational etiology of lung cancer /
Notice bibliographique
Résumé
The occupational environment has been a fruitful source of research on causes of cancer. Analyses in studies of occupational risk factors for cancer can experience problems if an attempt is made to model large numbers of exposures, some of which may be highly correlated. Typical analyses of such studies focus on one chemical at a time, but this may not adequately deal with mutual confounding. Based on a large study in Montreal, the objective of this thesis was twofold: to assess several occupational chemicals for their etiologic role in lung cancer, and to explore the use of semi-Bayes modeling to simultaneously estimate the effects of many chemicals at a time. Methods. Data came from a multiple-cancer case-control study of exposures in the work place. The study was comprised of 857 cases of lung cancer and 2172 controls consisting of patients with other types of cancer diagnosed from 1979 to 1985. Detailed occupational histories were collected and occupational hygienists translated these into exposure histories for 231 chemicals. All chemicals were analysed with conventional modeling strategies of both single and multiple parameter models. Of the 231 chemicals, 184 were singled out for analysis in a single large semi-Bayes model, which is a variant of classical empirical Bayes. This analysis is a fairly novel method suited to estimating large numbers of parameters in the face of sparse data. For the Bayesian portion of this model, chemicals were grouped by shared chemical and physical properties, based on the belief that these shared properties would imply similar effects on the risk of lung cancer. Results. Estimates for all 231 chemicals were derived under the various modeling strategies. For most chemicals, estimates changed little across these analytic approaches, though some differences were apparent. Of the 231 chemicals assessed, 53 were earmarked as requiring further evaluation and underwent additional analyses. Discussion. While semi-Bayes models have been shown previously to offer improved estimation over conventional analyses, the gains in using semi-Bayes models in the present study were less clear. Effort put into some portions of the Bayesian modeling did not materially influence the results. A number of chemicals were earmarked as potential lung carcinogens.
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 | 0,018 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».