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publications.bib

@@ -343,25 +343,6 @@ @dependabot[bot])
 	keywords = {datalad, BIDS},
 }
 
-@article{rosenblatt_data_2024,
-	title = {Data leakage inflates prediction performance in connectome-based machine learning models},
-	volume = {15},
-	issn = {2041-1723},
-	url = {https://www.nature.com/articles/s41467-024-46150-w},
-	doi = {10.1038/s41467-024-46150-w},
-	abstract = {Abstract
-            Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling.},
-	language = {en},
-	number = {1},
-	urldate = {2024-03-19},
-	journal = {Nature Communications},
-	author = {Rosenblatt, Matthew and Tejavibulya, Link and Jiang, Rongtao and Noble, Stephanie and Scheinost, Dustin},
-	month = feb,
-	year = {2024},
-	pages = {1829},
-	file = {Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:/home/alpron/Zotero/storage/78MZZXHW/Rosenblatt et al. - 2024 - Data leakage inflates prediction performance in co.pdf:application/pdf},
-}
-
 @article{dafflon_guided_2022,
 	title = {A guided multiverse study of neuroimaging analyses},
 	volume = {13},
@@ -514,7 +495,7 @@ @article{giehl_sharing_2024
 	file = {Giehl et al. - 2024 - Sharing brain imaging data in the Open Science era.pdf:/home/alpron/Zotero/storage/KLC63VH2/Giehl et al. - 2024 - Sharing brain imaging data in the Open Science era.pdf:application/pdf},
 }
 
-@article{rosenblatt_data_2024-1,
+@article{rosenblatt_data_2024,
 	title = {Data leakage inflates prediction performance in connectome-based machine learning models},
 	volume = {15},
 	issn = {2041-1723},
@@ -717,7 +698,7 @@ @article{tendler_why_2023
 	author = {Tendler, Benjamin C and Welland, Maddie and Miller, Karla L and {The WIN Handbook Team}},
 	month = jul,
 	year = {2023},
-	keywords = {best practices, to read},
+	keywords = {to read, best practices},
 	pages = {e88853},
 	file = {Tendler et al. - 2023 - Why every lab needs a handbook.pdf:/home/alpron/Zotero/storage/75L3MYKQ/Tendler et al. - 2023 - Why every lab needs a handbook.pdf:application/pdf},
 }
@@ -753,3 +734,57 @@ @article{isager_exploring_2024
 	pages = {330--346},
 	file = {Isager et al. - 2024 - Exploring a formal approach to selecting studies f.pdf:/home/alpron/Zotero/storage/Z2VP3RRD/Isager et al. - 2024 - Exploring a formal approach to selecting studies f.pdf:application/pdf},
 }
+
+@article{mandl_addressing_2024,
+	title = {Addressing researcher degrees of freedom through {minP} adjustment},
+	volume = {24},
+	issn = {1471-2288},
+	url = {https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02279-2},
+	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.},
+	language = {en},
+	number = {1},
+	urldate = {2024-08-01},
+	journal = {BMC Medical Research Methodology},
+	author = {Mandl, Maximilian M. and Becker-Pennrich, Andrea S. and Hinske, Ludwig C. and Hoffmann, Sabine and Boulesteix, Anne-Laure},
+	month = jul,
+	year = {2024},
+	keywords = {multiverse, to read},
+	pages = {152},
+	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},
+}
+
+@article{field_consequences_2024,
+	title = {Consequences of the {Scientfic} {Reform} {Movement}},
+	volume = {4},
+	issn = {2667-1204},
+	doi = {https://doi.org/10.36850/jote.i4.1},
+	language = {en},
+	number = {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.},
+	month = may,
+	year = {2024},
+	note = {Special Issue},
+}