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:
Approximately one-third of patients with HER2-positive breast cancer experienced recurrence within 10 years after receiving 1 year of adjuvant trastuzumab. The ExteNET study showed that 1 year of extended adjuvant neratinib after trastuzumab-based adjuvant therapy could reduce invasive disease-free survival (iDFS) events compared with placebo. This study investigated the efficacy and safety of pyrotinib, an irreversible pan-HER receptor tyrosine kinase inhibitor, after trastuzumab-based adjuvant therapy in patients with high-risk, HER2-positive early or locally advanced breast cancer.
Methods:
This multicenter phase II trial was conducted at 23 centers in China. After enrollment, patients received 1 year of extended adjuvant pyrotinib (400 mg/day), which should be initiated within 6 months after the completion of 1-year adjuvant therapy (trastuzumab alone or plus pertuzumab). The primary endpoint was 2-year iDFS rate.
Results:
Between January 2019 and February 2022, 141 eligible women were enrolled and treated. As of October 10, 2022, the median follow-up was 24 (interquartile range, 18.0–34.0) months. The 2-year iDFS rate was 94.59% (95% confidence interval [CI]: 88.97–97.38) in all patients, 94.90% (95% CI: 86.97–98.06) in patients who completed 1-year treatment, 90.32% (95% CI: 72.93–96.77) in patients who completed only 6-month treatment, 96.74% (95% CI: 87.57–99.18) in the hormone receptor (HR)-positive subgroup, 92.77% (95% CI: 83.48–96.93) in the HR-negative subgroup, 96.88% (95% CI: 79.82–99.55) in the lymph node-negative subgroup, 93.85% (95% CI: 86.81–97.20) in the lymph node-positive subgroup, 97.30% (95% CI: 82.32–99.61) in patients with adjuvant trastuzumab plus pertuzumab, and 93.48% (95% CI: 86.06–97.02) in patients with adjuvant trastuzumab. The most common adverse events were diarrhea (79.4%), fatigue (36.9%), lymphocyte count decreased (36.9%), nausea (33.3%), and hand-foot syndrome (33.3%).
Conclusions:
Extended adjuvant pyrotinib administrated after trastuzumab-based adjuvant therapy showed promising efficacy in patients with high-risk HER2-positive breast cancer. The follow-up is ongoing to determine the long-term benefit.
Funding:
No external funding was received for this work.
Clinical trial number:
ClinicalTrials.gov: NCT05880927
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Preeclampsia (PE), a major cause of maternal and perinatal mortality with highly heterogeneous causes and symptoms, is usually complicated by gestational diabetes mellitus (GDM). However, a comprehensive understanding of the immune microenvironment in the placenta of PE and the differences between PE and GDM is still lacking. In this study, cytometry by time of flight indicated that the frequencies of memory-like Th17 cells (CD45RA−CCR7+IL-17A+CD4+), memory-like CD8+ T cells (CD38+CXCR3−CCR7+Helios−CD127−CD8+) and pro-inflam Macs (CD206−CD163−CD38midCD107alowCD86midHLA-DRmidCD14+) were increased, while the frequencies of anti-inflam Macs (CD206+CD163−CD86midCD33+HLA-DR+CD14+) and granulocyte myeloid-derived suppressor cells (gMDSCs, CD11b+CD15hiHLA-DRlow) were decreased in the placenta of PE compared with that of normal pregnancy (NP), but not in that of GDM or GDM&PE. The pro-inflam Macs were positively correlated with memory-like Th17 cells and memory-like CD8+ T cells but negatively correlated with gMDSCs. Single-cell RNA sequencing revealed that transferring the F4/80+CD206− pro-inflam Macs with a Folr2+Ccl7+Ccl8+C1qa+C1qb+C1qc+ phenotype from the uterus of PE mice to normal pregnant mice induced the production of memory-like IL-17a+Rora+Il1r1+TNF+Cxcr6+S100a4+CD44+ Th17 cells via IGF1–IGF1R, which contributed to the development and recurrence of PE. Pro-inflam Macs also induced the production of memory-like CD8+ T cells but inhibited the production of Ly6g+S100a8+S100a9+Retnlg+Wfdc21+ gMDSCs at the maternal–fetal interface, leading to PE-like symptoms in mice. In conclusion, this study revealed the PE-specific immune cell network, which was regulated by pro-inflam Macs, providing new ideas about the pathogenesis of PE.
