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Panel stacking is a threat to consensus statement validity

delete2024-09-01
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PRE
AI
K
Kasper P. Kepp
M
Marcel Ballin
H
Howard Bauchner
E
Eran Bendavid
D
Daniel T. Blumstein
A
Adam Brufsky
G
Gordon Guyatt
G
George Hajishengallis
T
Tammy Hoffmann
T
Terry P. Klassen
E
Ellen Kuhl
C
Carlo La Vecchia
P
Paul W. Noble
A
Anna Nordström
P
Peter Nordström
C
Chirag J Patel
A
Andrea Saltelli
M
Manuel Schabus
H
Holger J. Schünemann
H
Hendrik Streeck
J
Joachim P. Sturmberg
A
Athanasios Tsakris
DOI:10.1016/j.jclinepi.2024.111428delete
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Abstract

Abstract

En 中文
Consensus statements can be very influential in medicine and public health. Some of these statements use systematic evidence synthesis but others fail on this front. Many consensus statements use panels of experts to deduce perceived consensus through Delphi processes. We argue that stacking of panel members toward one particular position or narrative is a major threat, especially in absence of systematic evidence review. Stacking may involve financial conflicts of interest, but nonfinancial conflicts of strong advocacy can also cause major bias. Given their emerging importance, we describe here how such consensus statements may be misleading, by analyzing in depth a recent high-impact Delphi consensus statement on COVID-19 recommendations as a case example. We demonstrate that many of the selected panel members and at least 35% of the core panel members had advocated toward COVID-19 elimination (Zero-COVID) during the pandemic and were leading members of aggressive advocacy groups. These advocacy conflicts were not declared in the Delphi consensus publication, with rare exceptions. Therefore, we propose that consensus statements should always require rigorous evidence synthesis and maximal transparency on potential biases toward advocacy or lobbyist groups to be valid. While advocacy can have many important functions, its biased impact on consensus panels should be carefully avoided. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keywords:
Evidence based medicine
Consensus statements
Panel bias
Transparency
Competing interests
Guidelines

Journal

Journal of Clinical Epidemiology cover
Journal of Clinical Epidemiology
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