Merge pull request #18 from Inria-Empenn/hugoblox-import-publications

Hugo Blox Builder - Import latest publications

content/publication/field-consequences-2024/cite.bib

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+@article{field_consequences_2024,
+ doi = {https://doi.org/10.36850/jote.i4.1},
+ editor = {Field, Sarahanne M. and van Dongen, Noah and Tiokhin, Leo and 0'Mahony, Aoife and Kaplan, Rebecca and Visser, Alex and Robaard, Meike and Prinsen, Jip and Korna, Thomas F.K.},
+ issn = {2667-1204},
+ language = {en},
+ month = {May},
+ note = {Special Issue},
+ number = {1},
+ title = {Consequences of the Scientfic Reform Movement},
+ volume = {4},
+ year = {2024}
+}

content/publication/field-consequences-2024/index.md

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+---
+title: Consequences of the Scientfic Reform Movement
+authors:
+- Sarahanne M. Field
+- Noah van Dongen
+- Leo Tiokhin
+- Aoife 0'Mahony
+- Rebecca Kaplan
+- Alex Visser
+- Meike Robaard
+- Jip Prinsen
+- Thomas F.K. Korna
+date: '2024-05-01'
+publishDate: '2024-08-02T08:28:50.563187Z'
+publication_types:
+- article-journal
+doi: https://doi.org/10.36850/jote.i4.1
+---

content/publication/mandl-addressing-2024/cite.bib

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+@article{mandl_addressing_2024,
+ abstract = {Abstract
+
+When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers’ analytical choices, an issue also referred to as “researcher degrees of freedom”. Combined with selective reporting of the smallest
+p
+-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the “minP” adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal
+p
+-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative
+
+
+\$\$paO_2\$\$
+
+
+p
+a
+
+O
+2
+
+
+
+
+
+on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error—and thus the risk of publishing false positive results that may not be replicable.},
+ author = {Mandl, Maximilian M. and Becker-Pennrich, Andrea S. and Hinske, Ludwig C. and Hoffmann, Sabine and Boulesteix, Anne-Laure},
+ doi = {10.1186/s12874-024-02279-2},
+ file = {Mandl et al. - 2024 - Addressing researcher degrees of freedom through m.pdf:/home/alpron/Zotero/storage/DZMEFP7J/Mandl et al. - 2024 - Addressing researcher degrees of freedom through m.pdf:application/pdf},
+ issn = {1471-2288},
+ journal = {BMC Medical Research Methodology},
+ keywords = {multiverse, to read},
+ language = {en},
+ month = {July},
+ number = {1},
+ pages = {152},
+ title = {Addressing researcher degrees of freedom through minP adjustment},
+ url = {https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02279-2},
+ urldate = {2024-08-01},
+ volume = {24},
+ year = {2024}
+}

content/publication/mandl-addressing-2024/index.md

@@ -0,0 +1,42 @@
+---
+title: Addressing researcher degrees of freedom through minP adjustment
+authors:
+- Maximilian M. Mandl
+- Andrea S. Becker-Pennrich
+- Ludwig C. Hinske
+- Sabine Hoffmann
+- Anne-Laure Boulesteix
+date: '2024-07-01'
+publishDate: '2024-08-02T08:28:50.549122Z'
+publication_types:
+- article-journal
+publication: '*BMC Medical Research Methodology*'
+doi: 10.1186/s12874-024-02279-2
+abstract: Abstract  When different researchers study the same research question using
+  the same dataset they may obtain different and potentially even conflicting results.
+  This is because there is often substantial flexibility in researchers’ analytical
+  choices, an issue also referred to as “researcher degrees of freedom”. Combined
+  with selective reporting of the smallest p -value or largest effect, researcher
+  degrees of freedom may lead to an increased rate of false positive and overoptimistic
+  results. In this paper, we address this issue by formalizing the multiplicity of
+  analysis strategies as a multiple testing problem. As the test statistics of different
+  analysis strategies are usually highly dependent, a naive approach such as the Bonferroni
+  correction is inappropriate because it leads to an unacceptable loss of power. Instead,
+  we propose using the “minP” adjustment method, which takes potential test dependencies
+  into account and approximates the underlying null distribution of the minimal p
+  -value through a permutation-based procedure. This procedure is known to achieve
+  more power than simpler approaches while ensuring a weak control of the family-wise
+  error rate. We illustrate our approach for addressing researcher degrees of freedom
+  by applying it to a study on the impact of perioperative   $$paO_2$$   p a  O 2      on
+  post-operative complications after neurosurgery. A total of 48 analysis strategies
+  are considered and adjusted using the minP procedure. This approach allows to selectively
+  report the result of the analysis strategy yielding the most convincing evidence,
+  while controlling the type 1 error—and thus the risk of publishing false positive
+  results that may not be replicable.
+tags:
+- multiverse
+- to read
+links:
+- name: URL
+  url: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02279-2
+---