Mixture Model to Predict the Cumulative Incidence of Relapses in Follicular Lymphoma : Need for Longer Follow-up or Alternative Outcomes
Notice bibliographique
Résumé
Background: The effectiveness of treatments in follicular lymphoma is evaluated by the reduction in the rate of recurrence. One of the methodological difficulties is related to the presence of competitive events such as death or second primary malignancies. The related cumulative incidence functions (CIF) are always estimated by using the non-parametric Aalen-Johansen estimator and the associated predictors by using the Fine and Gray approach or by considering a cause-specific Cox model. In the present study, we investigated if the use of a parametric mixture model would provide additional information on the interpretation of the impact of treatments in the management of follicular lymphoma. Methods: We used the RELEVANCE study database (Morschhauser et al.NEJM 2018, JCO 2022) because of the prolonged follow-up of patients treated for follicular lymphoma. RELEVANCE study is a multicenter, phase 3 randomized clinical trial that evaluates rituximab lenalidomide (R 2) as compared to rituximab plus chemotherapy (R-chemo), in patients with previously untreated follicular lymphoma. All 1030 patients received a rituximab maintenance. Results: With a median follow up of 72 months, median progression-free survival (PFS) was not reached in both groups (Kaplan-Meier estimator): the 6-years PFS was 60% and 59% for R 2 and R-chemo, respectively (Cox model, HR = 1.03 [IC95% CI, 0.84 to 1.27]). The CIF of relapse at 72 months was 41% and 39% in the R 2 and R-chemo, respectively (Aalen-Johansen estimator, Figure 1). We did not highlight any significant difference between the treatments in terms of the CIF of relapse (Fine and Gray model, HR = 0.938 [IC95% CI, 0.754 to 1.17], for the same prognostic time, the CIF of death was 2,5% and 2,9% in the R 2 and R-chemo, respectively). We then performed the test with the mixture model, and confirmed there was no significant differences across groups at 72 months. Indeed, 64% and 96% of patients have not relapsed and died respectively. However the mixture model approach allows for extrapolation, and thus we predicted the long-term CIF of relapse to be 67% if patients were followed-up 20 years across groups. The mixture model extrapolation calculated that 60% of relapse would occurr between 72 months and 240 months (20 years). Conclusion: There was no difference at 72 months for survival across tests; but the mixture model, that allow extrapolation, suggested that a difference could be revealed with a greater follow-up. We concluded that future studies of survival in FL will require longer follow-up. This comment appears of particular importance in the context of the forthcoming immunotherapies such as bispecific and CAR-T cells. We can also propose for hematologic malignancies with a chronic evolution, FL, alternative primary outcomes not based on survival, allowing a shorter read out, such as the disability-adjusted life years (DALYs) or quality-adjusted life years (QALYs).
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 | 0,012 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».