Success in Meeting the Primary Endpoint in Phase III Trials: A Comparison of Industry and Cooperative Group Trials
Notice bibliographique
Résumé
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 Methods: All consecutive, published and unpublished, phase III cancer RCTs assessing treatment superiority and conducted by Canada's NCIC Clinical Trials Group (NCIC CTG) and GlaxoSmithKline (GSK) from 1980 to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.
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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 | 0,572 | 0,806 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,021 |
| Bibliométrie | 0,027 | 0,027 |
| Études des sciences et des technologies | 0,001 | 0,007 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».