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Enregistrement W2692832060 · doi:10.1007/s11999-017-5421-7

Cochrane in CORR ®: Industry Sponsorship and Research Outcome

2017· letter· en· W2692832060 sur OpenAlexaff
Tahira Devji, Jason W. Busse

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2017
Typeletter
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiquePharmaceutical industry and healthcare
Établissements canadiensMcMaster UniversityImpact
Organismes subventionnairesnon disponible
Mots-clésMedicineBlindingInterimPublication biasClinical trialHarmRandomized controlled trialGovernment (linguistics)Pharmaceutical industryMEDLINEReporting biasFamily medicineMeta-analysisSurgeryInternal medicineLaw

Résumé

récupéré en direct d'OpenAlex

Importance of the Topic In the past decade, the number of clinical trials funded by industry has substantially increased in the United States [9]. The drug and device industry now funds six times more clinical trials than the federal government [3]. This may be cause for concern, as publication agreements in which industry sponsors constrain academic authors’ independence are common [5] and several research articles suggest industry sponsorship is more likely to result in pro-industry findings, pro-industry conclusions, and suppression of negative results [1, 2, 6, 8, 11]. A recent example of industry manipulation involved a randomized trial on low-intensity pulsed ultrasound for tibial shaft fractures. The industry sponsor conducted an unplanned interim analysis and, on the grounds of no difference in effect between treatment and control, discontinued the trial early [4, 13]. This Cochrane review examined 75 articles investigating whether industry funding of drug and device studies is associated with conclusions that are more favorable to the sponsor [10]. The review concluded that industry-sponsored studies reported more favorable efficacy results, similar harm results, more favorable conclusions, and less concordance between study results and conclusions when compared to nonindustry-sponsored studies. Industry-funded trials were also more likely to be at low risk of bias due to blinding. Upon Closer Inspection This Cochrane review provided consistent evidence for the existence of an industry bias. Most included studies were categorized at high risk of bias; this assessment of risk of bias, however, was based on unvalidated criteria. Many of the included studies lacked information on study conduct and did not control for confounders that could influence the relationship between industry sponsorship and research outcomes. Furthermore, it is not clear if the review accounted for the effects of clustering. The inclusion of multiple reviews may have resulted in shared primary studies contributing more than once to pooled effect estimates, potentially overestimating the association [10]. Despite these limitations, this review provides convincing evidence that industry-sponsored studies are more likely to report more favorable efficacy results than nonindustry-sponsored studies. This Cochrane review identified a number of important findings in their subgroup analyses. For example, when restricted to studies at low risk of bias, the association between industry sponsorship and favorable results was stronger and industry support was associated with less reporting of harms. A subgroup analysis based on the type of intervention found that industry-funded drug studies were more likely to report favorable results, whereas industry-funded device trials were less likely to report favorable results. Ideally, the authors would have performed meta-regression considering all promising subgroup factors to explore which ones retained significance in an adjusted analysis. Take-home Messages This recently published Cochrane review found evidence that industry sponsorship is associated with more favorable results and conclusions, and this bias is not captured by standard risk-of-bias assessments. The review authors suggest that industry bias may be mediated by choice of comparators (such as placebo vs. current gold standard), dosing and timing of comparisons, choice of outcomes, selective analysis, and selective reporting [7]. When conducting a systematic review and meta-analysis, authors should capture and report the prevalence of industry funding among eligible primary studies and empirically explore, on an outcome-by-outcome basis, whether industry funding is associated with systematic differences in treatment effects. If so, and the subgroup effect is deemed credible based on established criteria [12], we believe review authors should focus on studies that are not funded by industry. If no credible subgroup effect is detected, then review authors can confidentially pool results from studies that are industry funded with those that are not.

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)0,034
score de la tête « metaresearch » (Gemma)0,244
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,966
Score d'incertitude au seuil0,279

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0340,244
Méta-épidémiologie (sens strict)0,0020,002
Méta-épidémiologie (sens large)0,0080,007
Bibliométrie0,0140,018
Études des sciences et des technologies0,0010,002
Communication savante0,0080,005
Science ouverte0,0030,005
Intégrité de la recherche0,0050,005
Charge utile insuffisante (le modèle a refusé de juger)0,0830,008

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 Opus0,863
Tête enseignante GPT0,734
Écart entre enseignants0,130 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
DomaineÉvaluation
GenreCommentaire

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

En bref

Citations7
Publié2017
Routes d'admission1
Résumé présentoui

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