Consensus-based guidance for conducting and reporting multi-analyst studies
- Eötvös Loránd University, Hungary
- Karolinska Institutet, Sweden
- Tilburg University, Netherlands
- University of Groningen, Netherlands
- University of Southern California, United States
- University of Amsterdam, Netherlands
- Dartmouth Collge, United States
- University of Muenster, Germany
- University of Rennes, France
- McLean Hospital, United States
- University of Missouri, United States
- National University of Singapore, Singapore
- University of New South Wales, Australia
- Stockholm School of Economics, Sweden
- University Hospital Basel, Switzerland
- Monash University, Australia
- University of Wisconsin-Madison, United States
- Ludwig-Maximilians-University, Germany
- University of Innsbruck, Austria
- Maastricht University, Netherlands
- Princeton University, United States
- University of Victoria, Canada
- Université Paris-Saclay, France
- University of Bristol, United Kingdom
- Center for Open Science and University of Virginia, United States
- Stanford University, United States
- University of Basel, Switzerland
- Tel Aviv University, Israel
- ESMT Berlin, Germany
- University College London, United Kingdom
- University of Sussex, United Kingdom
- University of Illinois, United States
- University of Virginia, United States
- University of Alberta, Canada
- University of Massachusetts Amherst, United States
- INSEAD, Singapore
Abstract
Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.
Data availability
All anonymized data as well as the survey materials are publicly shared on the Open Science Framework page of the project: https://osf.io/4zvst/. Our methodology and data-analysis plan were preregistered. The preregistration document can be accessed at: https://osf.io/dgrua.
Article and author information
Author details
Jojanneke A Bastiaansen
Rotem Botvinik-Nezer
Noah N N van Dongen
Johnny B van Doorn
Morton Ann Gernsbacher
Magnus Johannesson
Jean-Francois Mangin
Russell A Poldrack
Don van Ravenzwaaij
Martin Schweinsberg
Funding
Netherlands Organisations for Scientific Research (406-17-568)
- Alexandra Sarafoglou
Natural Sciences and Engineering Research Council of Canada (BP-546283-2020)
- Samuel St-Jean
Fonds de recherche du Québec – Nature et technologies (290978)
- Samuel St-Jean
European Research Council (726361)
- Jelte Wicherts
European Research Council (726361)
- Olmo R van den Akker
European Research Council (681466)
- Yoram K Kunkels
VIDI fellowship organisation (016.Vidi.188.001)
- Don van Ravenzwaaij
VENI fellowship grant (Veni 191G.037)
- Laura F Bringmann
National Science Foundation (1760052)
- Matthew J Salganik
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2021, Aczel et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Consensus-based guidance for conducting and reporting multi-analyst studies
eLife 10:e72185.
https://doi.org/10.7554/eLife.72185
Further reading
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- Medicine
Background:
Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a severe and deadly adverse event following ERCP. The ideal method for predicting PEP risk before ERCP has yet to be identified. We aimed to establish a simple PEP risk score model (SuPER model: Support for PEP Reduction) that can be applied before ERCP.
Methods:
This multicenter study enrolled 2074 patients who underwent ERCP. Among them, 1037 patients each were randomly assigned to the development and validation cohorts. In the development cohort, the risk score model for predicting PEP was established via logistic regression analysis. In the validation cohort, the performance of the model was assessed.
Results:
In the development cohort, five PEP risk factors that could be identified before ERCP were extracted and assigned weights according to their respective regression coefficients: –2 points for pancreatic calcification, 1 point for female sex, and 2 points for intraductal papillary mucinous neoplasm, a native papilla of Vater, or the pancreatic duct procedures (treated as ‘planned pancreatic duct procedures’ for calculating the score before ERCP). The PEP occurrence rate was 0% among low-risk patients (≤0 points), 5.5% among moderate-risk patients (1–3 points), and 20.2% among high-risk patients (4–7 points). In the validation cohort, the C statistic of the risk score model was 0.71 (95% CI 0.64–0.78), which was considered acceptable. The PEP risk classification (low, moderate, and high) was a significant predictive factor for PEP that was independent of intraprocedural PEP risk factors (precut sphincterotomy and inadvertent pancreatic duct cannulation) (OR 4.2, 95% CI 2.8–6.3; p<0.01).
Conclusions:
The PEP risk score allows an estimation of the risk of PEP prior to ERCP, regardless of whether the patient has undergone pancreatic duct procedures. This simple risk model, consisting of only five items, may aid in predicting and explaining the risk of PEP before ERCP and in preventing PEP by allowing selection of the appropriate expert endoscopist and useful PEP prophylaxes.
Funding:
No external funding was received for this work.
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- Medicine
Estrogen significantly impacts women’s health, and postmenopausal hypertension is a common issue characterized by blood pressure fluctuations. Current control strategies for this condition are limited in efficacy, necessitating further research into the underlying mechanisms. Although metabolomics has been applied to study various diseases, its use in understanding postmenopausal hypertension is scarce. Therefore, an ovariectomized rat model was used to simulate postmenopausal conditions. Estrogen levels, blood pressure, and aortic tissue metabolomics were analyzed. Animal models were divided into Sham, OVX, and OVX +E groups. Serum estrogen levels, blood pressure measurements, and aortic tissue metabolomics analyses were performed using radioimmunoassay, UHPLC-Q-TOF, and bioinformatics techniques. Based on the above research content, we successfully established a correlation between low estrogen levels and postmenopausal hypertension in rats. Notable differences in blood pressure parameters and aortic tissue metabolites were observed across the experimental groups. Specifically, metabolites that were differentially expressed, particularly L-alpha-aminobutyric acid (L-AABA), showed potential as a biomarker for postmenopausal hypertension, potentially exerting a protective function through macrophage activation and vascular remodeling. Enrichment analysis revealed alterations in sugar metabolism pathways, such as the Warburg effect and glycolysis, indicating their involvement in postmenopausal hypertension. Overall, this current research provides insights into the metabolic changes associated with postmenopausal hypertension, highlighting the role of L-AABA and sugar metabolism reprogramming in aortic tissue. The findings suggest a potential link between low estrogen levels, macrophage function, and vascular remodeling in the pathogenesis of postmenopausal hypertension. Further investigations are needed to validate these findings and explore their clinical implications for postmenopausal women.
