The DOI matches its Crossref record; 192 of the 192 references it cites were found in a registry or an index.
Challenges for assessing replicability in preclinical cancer biology
Open accessAbstract
We conducted the Reproducibility Project: Cancer Biology to investigate the replicability of preclinical research in cancer biology. The initial aim of the project was to repeat 193 experiments from 53 high-impact papers, using an approach in which the experimental protocols and plans for data analysis had to be peer reviewed and accepted for publication before experimental work could begin. However, the various barriers and challenges we encountered while designing and conducting the experiments meant that we were only able to repeat 50 experiments from 23 papers. Here we report these barriers and challenges. First, many original papers failed to report key descriptive and inferential statistics: the data needed to compute effect sizes and conduct power analyses was publicly accessible for just 4 of 193 experiments. Moreover, despite contacting the authors of the original papers, we were unable to obtain these data for 68% of the experiments. Second, none of the 193 experiments were described in sufficient detail in the original paper to enable us to design protocols to repeat the experiments, so we had to seek clarifications from the original authors. While authors were extremely or very helpful for 41% of experiments, they were minimally helpful for 9% of experiments, and not at all helpful (or did not respond to us) for 32% of experiments. Third, once experimental work started, 67% of the peer-reviewed protocols required modifications to complete the research and just 41% of those modifications could be implemented. Cumulatively, these three factors limited the number of experiments that could be repeated. This experience draws attention to a basic and fundamental concern about replication – it is hard to assess whether reported findings are credible.
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Citations
Cites
1 retracted
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Cited by
Investigating the replicability of preclinical cancer biology
Errington, T. M., Mathur, M., Soderberg, C. K., Denis, A., Perfito, N., Iorns, E., & Nosek, B. A. (2021) · eLife · doi:10.7554/elife.71601 · Cited by 462
“After the replication results were known, some reviewers and commentators offered hypotheses for why the findings might have differed from the original (Errington et al., 2021b).”
ResultSelf-citationArtificial Intelligence for Drug Discovery: Are We There Yet?
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“Lessons (174) from eLife's Reproducibility Project, which focused on cancer biology, highlight issues like weaker-than-previously-published (175) effects and inaccurate protocol descriptions (176),”
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Metadata
- Authors
- Errington, T. M., Denis, A., Perfito, N., Iorns, E., & Nosek, B. A.
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2021-12-07
- Volume
- 10
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W4200099901
- Cited by
- 267
- References
- 192
- FWCI
- 42.63
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0/ (vor)
- creativecommons.org/licenses/by/4.0/ (am)
- creativecommons.org/licenses/by/4.0/ (tdm)
- Funders
- Arnold Ventures
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.