Response to Drs. Igor Burstyn and George Luta’s letter: Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias)
Notice bibliographique
Résumé
We want to thank Drs. Burstyn & Luta (1) for their recognition of our recent study (2) suggesting that estimates of breast cancer risk following retrospective self-reported night shift work are inflated by recall bias. The main strength of the study was a gold standard based on individual, prospective, objective and detailed information on night shift work that allowed validation of self-reported night shift work obtained after breast cancer was diagnosed – the usual situation for case–control studies (3). The study confirmed what textbooks have long taught but rarely documented empirically (4–6). We also want to thank Drs. Burstyn & Luta for their advice on how we could have utilized this precious dataset not only for simple but also probabilistic and Bayesian quantitative bias analyses. Even if highly instructive, this may still require strong statistical involvement. Using data provided in our paper, Drs. Burstyn & Luta’s bias-corrected odds ratio (OR) estimate of breast cancer following night shift work was centered around 1.0 (95% credible interval 0.3–1.7) and suggests that recall bias could completely, and not only partly as in our analysis (OR 1.05; correctly computed 95% confidence interval 0.88–1.27), explain the observed associations between night shift work and breast cancer found in case–control studies with retrospective self-reported exposure information. This finding strengthens our concern that breast cancer studies based on retrospective self-reports of night shifts may not provide convincing evidence. The gold standard of this validation study was based on a cohort of healthcare workers with day-by-day night shift information from a pay roll register and has earlier been used for breast cancer risk assessment showing no increased risk (7). A recent Swedish study using comparable data neither showed an overall increased risk (8). However, these studies included only information on recent night shift work. The next step should be a follow up of the cohorts when information on more distant night shift work becomes available with an emphasis on analyses that explore the timing of shift work on breast cancer risk. References 1. Burstyn I, Luta G. Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias). Scand J Work Environ Health – online first. https://doi.org/10.5271/sjweh.4226 2. Vestergaard JM, Haug JN, Dalbøge A, Bonde JP, Garde AH, Hansen J et al. Validity of self-reported night shift work among women with and without breast cancer. Scand J Work Environ Health. 2024;50(3):152–7. https://doi.org/10.5271/sjweh.4142. 3. Cordina-Duverger E, Menegaux F, Popa A, Rabstein S, Harth V, Pesch B et al. Night shift work and breast cancer: a pooled analysis of population-based case-control studies with complete work history. Eur J Epidemiol. 2018; 33(4):369–79. https://doi.org/10.1007/s10654-018-0368-x. 4. Berrington de González A, Richardson DB, Schubauer-Berigan MK. Statistical methods in cancer research, Volume V. Bias assessment in case–control and cohort studies for hazard identification. Lyon, France: International Agency for Research on Cancer; 2024. 5. Checkoway H, Pearce N, Kriebel D. Research Methods in Occupational Epidemiology. New York: Oxford University Press 2004. p372. 6. Rothman KJ. Modern Epidemiology. Boston/Toronto: Little, Brown and Company; 1986. p358. 7. Vistisen HT, Garde AH, Frydenberg M, Christiansen P, Hansen AM, Hansen J et al. Short-term effects of night shift work on breast cancer risk: a cohort study of payroll data. Scand J Work Environ Health. 2017;43(1):59–67. https://doi.org/10.5271/sjweh.3603. 8. Gustavsson P, Bigert C, Andersson T, Kader M, Härmä M, Selander J et al. Night work and breast cancer risk in a cohort of female healthcare employees in Stockholm, Sweden. Occup Environ Med. 2023;80(7):372–6. https://doi.org/10.1136/oemed-2022-108673. Henrik Albert Kolstad, MD,1, 2 Jesper Medom Vestergaard, MIT,1 Jens Peter Bonde, MD,3 Sadie Costello, PhD,4 Annett Dalbøge, PhD,1 Åse Marie Hansen, PhD,5, 6 Ann Dyreborg Larsen, PhD,6 Anne Helene Garde, PhD 5, 6 1 Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. 2 Department of Clinical Medicine, Aarhus University, Denmark. 3 Department of Occupational and Environmental Medicine, Bispebjerg and Frederiksberg Hospital, Denmark. 4 Environmental Health Science, School of Public Health, University of California, Berkeley, USA. 5 The National Research Centre for the Working Environment, Denmark. 6 Department of Public Health, University of Copenhagen, Denmark. Correspondence to: Henrik Kolstad, Occupational and Environmental Medicine, Danish Ramazzini Centre, Aarhus University Hospital, Aarhus, Denmark. [E-mail: kolstad@clin.au.dk]
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».