Modeling drug exposures and their time-varying effects : comparison of statistical analysis methods
Notice bibliographique
Résumé
Assessing the effects of drug exposure on the occurrence of health events is a real challenge. As individuals' drug exposures are likely to vary over time, they carry associated risks that depend on the dose, duration, and timing of treatment. Thus, different study designs and statistical analysis methods are used to estimate the risk associated with drug exposure. However, few studies have systematically evaluated and compared the respective performances of cohort and nested case-control (NCC) designs to estimate the effect of fixed or time-varying drug exposure. In this thesis work, we used simulations to examine and compare the performance of estimates from NCC versus whole cohort analyses to assess associations between a fixed or time-varying exposure and the risk of a health event. For this comparison, we also used data from the E3N cohort to assess the association between the use of menopausal hormone therapy and breast cancer risk. The results of the simulation study we conducted showed that the estimates obtained from the analysis of the whole cohort were unbiased in all scenarios considered. However, the estimates from the NCC analyses were substantially biased, especially when only one control was matched to each case. This bias in the nested case-control estimates increased with the proportion of events. A significant improvement in the NCC estimates was observed after the use of a bias reduction method, suggesting that the observed biases could be the result of sparse data. However, we were not satisfied with this explanation as the biases persisted regardless of the number of events. We, therefore, pursued our investigations by looking at the handling of tied event times by evaluating different methods to take them into account in the NCC analysis. Our simulation study and application to the E3N cohort data showed that NCC analyses with Breslow or Efron approximations could lead to a significant bias when there were a large number of tied event times in the data. However, once the tied event times were properly accounted for using the exact method or an approach that allowed for a single case in each stratum, the NCC estimates were almost unbiased and close to those of the whole cohort analysis. We strongly recommend that particular attention be paid to tied events in CTN analyses, in particular how they are handled both when forming matched strata and in the analysis by conditional logistic regression.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 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 ».