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Addressing Common Misuses and Pitfalls of P values in Biomedical Research

In recent years, there has been a growing recognition that P values, albeit useful in reporting data analysis results, have often been misused or misinterpreted in biomedical research. The emergence of big health data such as genomics data and electronic health records, sometimes combined with inade...

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Detalles Bibliográficos
Autores principales: Wang, Ming, Long, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for Cancer Research 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354639/
https://www.ncbi.nlm.nih.gov/pubmed/35919988
http://dx.doi.org/10.1158/0008-5472.CAN-21-2978
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author Wang, Ming
Long, Qi
author_facet Wang, Ming
Long, Qi
author_sort Wang, Ming
collection PubMed
description In recent years, there has been a growing recognition that P values, albeit useful in reporting data analysis results, have often been misused or misinterpreted in biomedical research. The emergence of big health data such as genomics data and electronic health records, sometimes combined with inadequate experimental design, has exacerbated this problem, which has become a major cause of the ongoing crisis in reproducibility in biomedical research. We aim to shed light and raise awareness of common misuses and pitfalls of P values and discuss potential mitigation strategies that leverage state-of-the-art statistical methods. The best practices always start with a sound study design including a robust data collection strategy to minimize data bias and a carefully thought-out analysis plan that can address potential misuses and pitfalls of P values. We highly encourage biomedical researchers to engage and involve statisticians from the very beginning of their studies.
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spelling pubmed-93546392022-08-05 Addressing Common Misuses and Pitfalls of P values in Biomedical Research Wang, Ming Long, Qi Cancer Res Controversy and Consensus In recent years, there has been a growing recognition that P values, albeit useful in reporting data analysis results, have often been misused or misinterpreted in biomedical research. The emergence of big health data such as genomics data and electronic health records, sometimes combined with inadequate experimental design, has exacerbated this problem, which has become a major cause of the ongoing crisis in reproducibility in biomedical research. We aim to shed light and raise awareness of common misuses and pitfalls of P values and discuss potential mitigation strategies that leverage state-of-the-art statistical methods. The best practices always start with a sound study design including a robust data collection strategy to minimize data bias and a carefully thought-out analysis plan that can address potential misuses and pitfalls of P values. We highly encourage biomedical researchers to engage and involve statisticians from the very beginning of their studies. American Association for Cancer Research 2022-08-03 2022-08-03 /pmc/articles/PMC9354639/ /pubmed/35919988 http://dx.doi.org/10.1158/0008-5472.CAN-21-2978 Text en ©2022 The Authors; Published by the American Association for Cancer Research https://creativecommons.org/licenses/by/4.0/This open access article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
spellingShingle Controversy and Consensus
Wang, Ming
Long, Qi
Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title_full Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title_fullStr Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title_full_unstemmed Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title_short Addressing Common Misuses and Pitfalls of P values in Biomedical Research
title_sort addressing common misuses and pitfalls of p values in biomedical research
topic Controversy and Consensus
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354639/
https://www.ncbi.nlm.nih.gov/pubmed/35919988
http://dx.doi.org/10.1158/0008-5472.CAN-21-2978
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