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Methodological expectations for demonstration of health product effectiveness by observational studies - 09/02/25

Doi : 10.1016/j.therap.2024.10.062 
Michel Cucherat a, Olivier Demarcq b, Olivier Chassany c, Claire Le Jeunne d, e, Isabelle Borget f, g, 1, Cécile Collignon h, 1, Vincent Diebolt i, 1, Marion Feuilly j, 1, Béatrice Fiquet k, 1, Clémence Leyrat l, 1, Florian Naudet m, n, 1, Raphaël Porcher o, p, 1, Nathalie Schmidely q, 1, Tabassome Simon r, 1, Matthieu Roustit s,
a Metaevidence.org, service de pharmacologie, hospices civils de Lyon, 69000 Lyon, France 
b Pfizer Inc, Chief Medical Affairs Organization, Pfizer US Commercial Division, 75014 Paris, France 
c Unité de recherche clinique en économie de la santé (URC-ECO), hôpital Hôtel-Dieu, AP–HP, 75004 Paris, France 
d Université Paris Cité, AP–HP, 75000 Paris, France 
e Hôpital Cochin, 75014 Paris, France 
f Gustave Roussy, Biostatistics and Epidemiology Office, université Paris-Saclay, 94810 Villejuif, France 
g Inserm, université Paris-Saclay, CESP U1018, Oncostat, labeled Ligue contre le cancer, 94810 Villejuif, France 
h Medtronic, 78620 L’Etang-la-Ville, France 
i F-CRIN Coordination, 31300 Toulouse, France 
j Bayer HealthCare SAS, département accès au marché, 59045 Lille, France 
k Amgen SAS, 92400 Courbevoie, France 
l Department of Medical Statistics, London School of Hygiene & Tropical Medicine, WC1E 7HT3 London, United Kingdom 
m University of Rennes, CHU Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) -UMR-S 1085, centre d’investigation clinique de Rennes (CIC1414), 35000 Rennes, France 
n University Institute of France, 75000 Paris, France 
o Université Paris Cité, université Sorbonne Paris Nord, Inserm, INRAe, Centre for Research in Epidemiology and Statistics (CRESS), hôpital Hôtel-Dieu, 75004 Paris, France 
p Centre d’épidémiologie clinique, hôpital Hôtel-Dieu, AP–HP, 75000 Paris, France 
q Takeda France, accès des patients à l’innovation, 75000 Paris, France 
r Service de pharmacologie, plateforme de recherche clinique de l’Est parisien, Sorbonne université, AP–HP, 75012 Paris, France 
s University Grenoble Alpes, Inserm, CIC1406, HP2 U1300, CHU Grenoble Alpes, 38043 Grenoble, France 

Corresponding author. Centre d’investigation clinique, Inserm CIC1406, CHU Grenoble Alpes, 38043 Grenoble cedex, France.Centre d’investigation clinique, Inserm CIC1406, CHU Grenoble AlpesGrenoble cedex38043France

Summary

The issue of assessing the effectiveness of health technologies (drugs, devices, etc.) through observational studies is becoming increasingly important as registration and market access agencies consider them in their evaluation process. In this context, observational studies must be able to provide real demonstrations of a level of reliability comparable to those produced by the conventional randomized controlled trial (RCT) approach. The objective of the roundtable was to establish the acceptability criteria for an observational study (non-randomized, non-interventional study) to be able to provide these demonstrations, and possibly serve as a confirmatory study for registration and market access authorities, the construction of therapeutic strategies or the development of recommendations. In order to do this, the study must be a real confirmatory study respecting the hypothetical-deductive approach and guaranteeing the absence of HARKing and p-hacking by attesting to the establishment of a protocol and a statistical analysis plan, recorded before any inferential analysis. It must also be part of a formalized approach to causal inference and demonstrate that it correctly identifies the causal estimand sought. The study should ensure that there is no residual confusion bias by taking into account all confounding factors affecting the comparison, which should be determined by a formal approach (such as a graphical causality approach, DAGs). Residual confusion bias diagnoses by forgery and nullification analysis should be non-existent. The study shall be at low risk of bias, in particular selection bias, among others by using a target test emulation design. Overall type I error risk should be strictly controlled. The absence of selective publication of results and selection bias should be ensured.

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Keywords : Observational studies, Real world data, Real world evidence, Health technology assessment, Critical reading, Decision-making, Regulatory agencies, Market access


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© 2024  The Author(s). Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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P. 47-59 - gennaio 2025 Ritorno al numero
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  • Attentes méthodologiques pour la démonstration de l’efficacité des produits de santé par les études observationnelles
  • Michel Cucherat, Olivier Demarcq, Olivier Chassany, Claire Le Jeunne, Isabelle Borget, Cécile Collignon, Vincent Diebolt, Marion Feuilly, Béatrice Fiquet, Clémence Leyrat, Florian Naudet, Raphaël Porcher, Nathalie Schmidely, Tabassome Simon, Matthieu Roustit
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