RE: Aspirin, Ibuprofen, and the Risk for Colorectal Cancer in Lynch Syndrome
Notice bibliographique
Résumé
We read with interest the study by Ait Ouakrim et al. (1) that appeared in a recent issue of the Journal. In this study, conducted in a cohort of patients with Lynch syndrome, ever use of aspirin and ever use of ibuprofen were both associated with a strong overall decreased risk of colorectal cancer (hazard ratio [HR] = 0.48, 95% confidence interval [CI] = 0.33 to 0.71, and HR = 0.33, 95% CI = 0.20 to 0.55, respectively). We believe these effect estimates may have been exaggerated by immortal time bias (2). In this study, exposure to aspirin and ibuprofen was retrospectively assessed via questionnaires administered at the time of recruitment. Patients were asked whether they had “taken aspirin or ibuprofen at least twice a week for a month or longer.” Based on the response, patients were classified into one of two exposure groups: ever vs never use. These exposure groups were then followed from birth until their age at the time of colorectal cancer diagnosis or censoring because of the end of follow-up. However, this time-fixed exposure definition ignores the age at which exposure to the drug of interest was initiated and thus introduces immortal time bias. Specifically, based on this exposure definition, all ever users were considered to have been exposed during the entire follow-up period, that is, from birth until the event or end of follow-up. This introduces two methodological issues. First, the time between birth and the age at first exposure is incorrectly classified as exposed person-time. Second, by design, it is impossible for patients to have an event during this misclassified time period as an event would result in their reclassification as never users (Figure 1). The inclusion of this misclassified immortal time in the denominator of the rate for the exposure group can lead to spurious “protective” effects (3). The time-fixed approach introduces immortal time bias by defining exposure at cohort entry (in this case, birth). To be classified as exposed, patients must survive without colorectal cancer up to the age of first exposure to aspirin. This period of immortal time is classified as exposed even though the drug was not actually taken. This misclassified immortal person-time inflates the denominator of the incidence rate of the exposed, thus resulting in a rate ratio that is biased away from the null. In the correct, time-dependent approach, each patient may contribute person-time to both the exposed and the unexposed groups. While the authors did not provide information regarding the mean age at first exposure, it is likely that the majority of patients became regular users of aspirin and ibuprofen later in life. In this context, this would represent thousands of unexposed person-years of follow-up misclassified as exposed. It would thus be informative if the authors repeated their analyses with exposure to aspirin and ibuprofen defined using a time-dependent approach (2), which would correctly classify the unexposed and exposed person-time for all patients included in the cohort. We believe that such an approach would provide a more valid estimate of the potential chemopreventive effects of aspirin and ibuprofen on the incidence of colorectal cancer.
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 | 0,002 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,025 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,005 |
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 ».