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Article Dans Une Revue Alternative and Complementary Therapies Année : 2007

Integrity of Scientific Data : Transparency of Clinical Trial Data

Yves Juillet
  • Fonction : Auteur
Pierre-Henri Bertoye
  • Fonction : Auteur
Christophe Baduel
  • Fonction : Auteur
Corinne Bernaud
  • Fonction : Auteur
Dominique Doucet
  • Fonction : Auteur
Thérèse Dupin-Spriet
  • Fonction : Auteur
Valérie Foltzer
  • Fonction : Auteur
Danielle Golinelli
  • Fonction : Auteur
Sylvie Hansel
  • Fonction : Auteur
Jean-Marc Husson
  • Fonction : Auteur
  • PersonId : 918442
Rémi Lecoent
  • Fonction : Auteur
François Pelen
  • Fonction : Auteur
Catherine Rey-Quino
  • Fonction : Auteur
Jean-Charles Reynier
  • Fonction : Auteur
Tabassome Simon
Anne-Priscille Vlasto
  • Fonction : Auteur
Beat Widler
  • Fonction : Auteur
Faiez Zannad

Résumé

The integrity of the data from clinical trials and of its use is an essential element of the scientific method, and of the trust one can have in this method. There are many examples of fraud, and they recur regularly. The objective of this round table was to work on the definition of fraud, on its recognition and prevention especially in the institutional system. Fraud involves an active decision to cheat, and ranges from trying to hide incompetence to wholesale invention of data, patients or studies. Its frequency is difficult to evaluate but might be as high as 1% of all studies or publications. Fraud can involve ethics (post-hoc IRB [institutional review board] approval, IRB requests not applied, lack of consent), or any of the steps from realisation to interpretation of studies or trials. Identification of fraud is made harder by the usual risk for the whistle-blowers, who must be protected. Seeking fraud is implicit in Good Clinical Practices (GCP) that all industry sponsors must apply, but that are less often applied by institutional sponsors. It might be useful to install procedures to detect fraud in studies, especially institutional. Various statistical methods can be used to identify unusual data patterns that could suggest fraud. Once fraud is identified, its management is often not foreseen. Here again, clear procedures or recommendations would be of help.

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Dates et versions

halshs-02573416 , version 1 (14-05-2020)

Identifiants

Citer

Nicholas Moore, Yves Juillet, Pierre-Henri Bertoye, Christophe Baduel, Corinne Bernaud, et al.. Integrity of Scientific Data : Transparency of Clinical Trial Data. Alternative and Complementary Therapies, 2007, 62 (3), pp.211-216. ⟨10.2515/therapie:2007043⟩. ⟨halshs-02573416⟩
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