An Investigation of Potential Treatment Effect Modifiers in Multiple Myeloma from Published Randomized Controlled Trials
Notice bibliographique
Résumé
Purpose: The purpose of this study was to identify treatment effect modifiers (TEMs) of progression-free survival (PFS) and overall survival (OS) in multiple myeloma (MM) using published data from randomized controlled trials (RCTs). Background TEMs are clinical or demographic characteristics which impact the relationship between treatments and outcomes. Effectiveness of treatments vary across different subgroups defined by the TEM. To ensure accurate determination of outcome estimates, it is crucial to obtain a systematic and up-to-date list of potential TEMs prior to conducting clinical trials or cross-trial comparisons, such as population-adjusted indirect comparisons (PAICs), in a dynamically evolving treatment landscape. Currently, such an evaluation of TEMs based on RCTs across MM populations is limited. Materials and Methods: A literature review was conducted to identify RCTs published between January 1996 and April 2023 for newly-diagnosed (ND) transplant-eligible (TE) or transplant-ineligible (TIE) and relapsed/refractory (RR) MM patient populations. The primary source of information was PubMed, supplemented by searches in ClinicalTrials.gov, other websites (i.e., for drug manufacturer press releases, conferences abstracts/posters/presentations, as well as regulatory reviews from the United States Food and Drug Administration and the European Medicines Agency), and bibliographies of on-topic reviews. Subgroup analyses reporting relative PFS or OS effects of the treatment arm versus control in at least two levels of the subgroup variable were reviewed. Hazard ratios (HRs) and confidence intervals (CIs) for each level were extracted. Treatment effect differences across levels were evaluated by determining the CI of the ratio between HRs from two levels. Potential TEMs were identified within each RCT by noting subgroup variables that were significant at P<0.2. A subgroup variable was considered likely a TEM if it met this threshold in at least three unique RCTs. The 80% CI threshold (P<0.2) was chosen because subgroup analyses in RCTs are generally underpowered due to smaller sample sizes compared to the main analysis. Results: Data from 65 RCTs were included (NDMM TE = 15, NDMM TIE = 21, RRMM = 29). Variables considered as TEMs for PFS across different MM groups were cytogenetic risk, International Staging System (ISS)/revised-ISS stage, age, and sex. Additional TEMs for NDMM TIE included creatinine clearance and ECOG score; and for RRMM included creatinine clearance, refractory and prior therapy exposure status. The variables considered as TEMs for OS were cytogenetic risk for NDMM TIE; cytogenetic risk, age, ISS/r-ISS stage and geographical region for RRMM. No variable(s) met the threshold of TEM for OS in the NDMM TE population. Conclusions: This study identified potential patient demographic and clinical characteristics that may affect the likelihood of MM treatment response. These characteristics should be considered in future trial designs and PAICs to minimize potential bias when comparing treatment effects across different MM patient populations.
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 | 0,167 | 0,416 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,013 | 0,024 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».