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10.3390/ijerph18179304 http://scihub22266oqcxt.onion/10.3390/ijerph18179304 34501892!8430657!34501892     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Environ+Res+Public+Health 2021 ; 18 (17): ä Nephropedia Template TP {{tp|p=34501892|t=2021. Multiplicity Eludes Peer Review: The Case of COVID-19 Research |pdf=|usr=}}{{34501892}} gab.com Text Multiplicity Eludes Peer Review: The Case of COVID-19 Research JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34501892 #FOAvip Multiplicity arises when data analysis involves multiple simultaneous inferences, increasing the chance of spurious findings. It is a widespread problem frequently ignored by researchers. In this paper, we perform an exploratory analysis of the Web of Science database for COVID-19 observational studies. We examined 100 top-cited COVID-19 peer-reviewed articles based on p-values, including up to 7100 simultaneous tests, with 50% including >34 tests, and 20% > 100 tests. We found that the larger the number of tests performed, the larger the number of significant results (r = 0.87, p < 10(-6)). The number of p-values in the abstracts was not related to the number of p-values in the papers. However, the highly significant results (p < 0.001) in the abstracts were strongly correlated (r = 0.61, p < 10(-6)) with the number of p < 0.001 significances in the papers. Furthermore, the abstracts included a higher proportion of significant results (0.91 vs. 0.50), and 80% reported only significant results. Only one reviewed paper addressed multiplicity-induced type I error inflation, pointing to potentially spurious results bypassing the peer-review process. We conclude the need to pay special attention to the increased chance of false discoveries in observational studies, including non-replicated striking discoveries with a potentially large social impact. We propose some easy-to-implement measures to assess and limit the effects of multiplicity. Twit Text FOAVip Multiplicity Eludes Peer Review: The Case of COVID-19 Research ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34501892 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34501892 #FOAvip English Wikipedia <ref>{{Cite pmid|34501892}}</ref> Multiplicity Eludes Peer Review: The Case of COVID-19 Research #MMPMID34501892 Gutierrez-Hernandez O; Garcia LV Int J Environ Res Public Health 2021[Sep]; 18 (17): ä PMID34501892show ga Multiplicity arises when data analysis involves multiple simultaneous inferences, increasing the chance of spurious findings. It is a widespread problem frequently ignored by researchers. In this paper, we perform an exploratory analysis of the Web of Science database for COVID-19 observational studies. We examined 100 top-cited COVID-19 peer-reviewed articles based on p-values, including up to 7100 simultaneous tests, with 50% including >34 tests, and 20% > 100 tests. We found that the larger the number of tests performed, the larger the number of significant results (r = 0.87, p < 10(-6)). The number of p-values in the abstracts was not related to the number of p-values in the papers. However, the highly significant results (p < 0.001) in the abstracts were strongly correlated (r = 0.61, p < 10(-6)) with the number of p < 0.001 significances in the papers. Furthermore, the abstracts included a higher proportion of significant results (0.91 vs. 0.50), and 80% reported only significant results. Only one reviewed paper addressed multiplicity-induced type I error inflation, pointing to potentially spurious results bypassing the peer-review process. We conclude the need to pay special attention to the increased chance of false discoveries in observational studies, including non-replicated striking discoveries with a potentially large social impact. We propose some easy-to-implement measures to assess and limit the effects of multiplicity. |*COVID-19[MESH] |Humans[MESH] |Peer Review[MESH] |Probability[MESH]Multiplicity Eludes Peer Review: The Case of COVID-19 Research (epmc).pdf DeepDyve Pubget Overpricing