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Legal and ethical issues associated with patient recruitment in clinical trials: the case of competitive enrolment.

2005· article· en· W23660650 sur OpenAlexaff
Timothy Caulfield

Notice bibliographique

RevuePubMed · 2005
Typearticle
Langueen
DomaineMedicine
ThématiqueBiomedical Ethics and Regulation
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésClinical trialGovernment (linguistics)BusinessThe InternetPublic relationsMarketingMedicinePolitical sciencePathology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Introduction The demand for patients for clinical trials continues to increase. There are more clinical trials being done (often involving patients with similar conditions), government regulators require an increasing amount of data for the approval process, and some within industry have speculated that patients are becoming less willing to participate. These pressures have led to the development of a variety strategies to make the recruitment of patients more efficient and effective, such as the creation of research networks, the implementation of software to determine patient eligibility (1) and the use of email and the internet to find new patients. (2) Indeed, patient recruitment has become an industry. Competitive enrolment has emerged as one of the most common patient recruitment practices. Despite being ubiquitous, there is surprisingly little literature on the nature and ethical implications of competitive enrolment. This paper briefly considers the issues associated with this recruitment scheme. I will argue that competitive enrolment creates significant ethical challenges that need to be addressed by both REBs and at the level of national research ethics policy. Competitive Enrolment From the perspective of industry, patient recruitment is seen as a critical issue. In a paper written by an industry consultant, it is claimed that only 15% of clinical trials are completed on time, with over 50% of delays attributed to patient recruitment and 30% of investigator sites failing to recruit a single patient. (3) The authors also suggest that the estimated cost of patient recruitment is $1.89 billion. These costs are subject to further increases with each day's delay in bringing the product to market. (4) In another industry document it is stated that drug companies stand to lose between $600,000 and $8 million each day clinical trials delay a drug's development and launch. (5) It shouldn't be forgotten how much the industry has invested in the research and development process. Though estimates vary considerably, one paper suggests that it takes nearly eight years to develop a drug, almost twice as long as it took 20 years ago and, quoting from a study by the Tufts Center for the Study of Drug Development, $1 billion per drug, from concept to market. (6) While such figures often come from industry sources, there is no doubt that increasing access to patients and promoting patient participation in clinical trials has become an industry priority. A lot of money is at stake. For sponsoring companies, encouraging patients to participate and to complete clinical trials has a direct relationship to profit and the success of a new product. As such, it is understandable that sponsoring companies would want to devise strategies to optimize recruitment. One such strategy is competitive enrolment. Indeed, it is frequently viewed as an essential part of the overall patient recruitment plan. As noted by one recruitment consultant: We strongly recommend the use of competitive enrolment together with the inclusion of backup sites so they can be brought on board should individual sites drop below their agreed target levels. (7) Competitive enrolment is often part of the clinical trial agreement between the investigators and the sponsor of the trial--usually a pharmaceutical company. The goal, of course, is the advancement of rapid patient recruitment. It works by pitting investigating sites against one another. In return for involvement in the protocol and remuneration (which is often generous), (8) investigators agree to recruit a specific number of patients (often within a specified period of time). Such arrangements create a significant incentive for investigators to recruit patients as fast as they can. In a sense, they are in a race with other sites. If the clinical site does not meet a specified recruitment target, the sponsoring company may have the option to drop them from the protocol. …

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,977
Score d'incertitude au seuil0,285

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,192
Tête enseignante GPT0,427
Écart entre enseignants0,235 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
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

Citations13
Publié2005
Routes d'admission1
Résumé présentoui

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