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Enregistrement W2508758106 · doi:10.1182/blood.v118.21.508.508

Success in Meeting the Primary Endpoint in Phase III Trials: A Comparison of Industry and Cooperative Group Trials

2011· article· en· W2508758106 sur OpenAlexaboutno aff
Benjamin Djulbegović, Ambuj Kumar, Branko Miladinović, Asmita Mhaskar, Tea Reljic, Sanja Galeb, Rahul Mhaskar, Iztok Hozo, Dongsheng Tu, Heather Stanton, Christopher M. Booth, Ralph M. Meyer

Notice bibliographique

RevueBlood · 2011
Typearticle
Langueen
DomaineMedicine
ThématiqueEthics in Clinical Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRandomized controlled trialClinical trialMedicineResearch designPublication biasClinical endpointFamily medicineActuarial sciencePsychologyMeta-analysisInternal medicineStatisticsEconomics

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 508 Background: Evaluation of research effort, especially estimation of the proportion of treatment successes in randomized clinical trials (RCTs), has important ethical, scientific, and public policy implications. Whether commercial or public sector research programs generate higher discovery rate of new successful treatments when tested in cancer RCTs is not known. These research programs are postulated to be governed by two competing hypotheses. The “equipoise/uncertainty hypothesis” assumes that investigators cannot predict trial results in advance, and as a consequence, the rate of discovering new treatments is about 50%. In contrast, the “design bias hypothesis” assumes that researchers conduct only those RCTs which have high likelihood of success. We hypothesize that the public sector RCTs are governed by the equipoise hypothesis while the industry-sponsored (IS) RCTs are based on the design bias hypothesis. Here we conduct the comparative systematic assessment to investigate if IS RCTs are associated with higher success rates than publicly-sponsored trials (PS) according to design bias versus equipoise/uncertainty hypothesis, respectively. Methods: All consecutive, published and unpublished, phase III cancer RCTs assessing treatment superiority and conducted by Canada's NCIC Clinical Trials Group (NCIC CTG) and GlaxoSmithKline (GSK) from 1980 to June 2010 were included. All trial protocols from GSK and NCIC CTG were reviewed independently by two reviewers to determine their eligibility. Two reviewers independently extracted data from eligible study protocols and publications using a standardized form. Three metrics were extracted to determine treatment successes: (1) the proportion of statistically significant trials favoring new or standard treatments, (2) the proportion of the trials in which new treatments were considered superior according to the original investigators, and (3) quantitative synthesis of data for primary outcomes as defined in each trial. An experimental regimen (drug compound or combinations or procedures), which was not tested previously in an RCT involving a specific cancer population or for alleviation of symptoms was classified as a major innovation. If a drug or regimen was already tested in a specific cancer population and testing involved dose modifications or changes in route of administration, it was classified as a minor innovation. Results: Between1980 to 2010 NCIC CTG conducted 77 RCTs enrolling 33,260 patients while GSK conducted 40 cancer RCTs accruing 19,889 patients. Forty two percent (99%CI 24 to 60) of the results were statistically significant favoring experimental treatments in GSK versus 25% (99%CI 13 to 37) in the NCIC CTG cohort (p=0.04). Investigators concluded that new treatments were superior to standard treatments in 80% of GSK versus 44% of NCIC CTG RCTs (p<0.0001) The GSK investigators deemed 32% (99%CI 14 to 50; 14/44) of interventions as “breakthroughs” versus 10% (99%CI 1 to 18; 8/82) by NCIC CTG investigators (p=0.002). Pooled analysis for the primary outcome indicated higher success rate in GSK trials (odds ratio: 0.61 [99%CI 0.47–0.78]) versus NCIC trials (odds ratio: 0.86 [99%CI 0.74–1.00]) (p=0.003). Experimental treatments were considered as major innovations in 32% (99%CI 15 to 49; 16/50) of GSK vs. 93% (99%CI 86 to 100; 78/84) of NCIC CTG trials (p<0.0001). Increased success rate in IS RCTs was mainly due to testing of new palliative agents, while the research program of NCIC CTG largely focused on development of therapies to improve survival. Conclusions: This first study evaluating the treatment success and pattern of therapeutic discoveries in IS versus PS research showed that industry discovers more successful new treatments compared with public sector. However, industry appears to undertake RCTs with high likelihood of success. PS research had significantly high proportion of major innovations compared with IS research. Disclosures: No relevant conflicts of interest to declare.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,572
score de la tête « metaresearch » (Gemma)0,806
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Méthodes · Signal consensuel: Méthodes
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,428
Score d'incertitude au seuil0,527

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,5720,806
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0100,021
Bibliométrie0,0270,027
Études des sciences et des technologies0,0010,007
Communication savante0,0090,008
Science ouverte0,0040,007
Intégrité de la recherche0,0050,003
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,681
Tête enseignante GPT0,599
Écart entre enseignants0,082 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineMéthodes
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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