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Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses

OBJECTIVES: Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structured criteria for peer reviewers to assess others’ clinical research manuscripts...

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Autores principales: Jing, Xia, Zhou, Yuchun, Cimino, James J., Shubrook, Jay H., Patel, Vimla L., De Lacalle, Sonsoles, Weaver, Aneesa, Liu, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882446/
https://www.ncbi.nlm.nih.gov/pubmed/36711561
http://dx.doi.org/10.1101/2023.01.17.23284666
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author Jing, Xia
Zhou, Yuchun
Cimino, James J.
Shubrook, Jay H.
Patel, Vimla L.
De Lacalle, Sonsoles
Weaver, Aneesa
Liu, Chang
author_facet Jing, Xia
Zhou, Yuchun
Cimino, James J.
Shubrook, Jay H.
Patel, Vimla L.
De Lacalle, Sonsoles
Weaver, Aneesa
Liu, Chang
author_sort Jing, Xia
collection PubMed
description OBJECTIVES: Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structured criteria for peer reviewers to assess others’ clinical research manuscripts or grant proposals. This study aimed to develop, test, validate, and use evaluation metrics and instruments to accurately, consistently, and conveniently assess the quality of scientific hypotheses for clinical research projects. MATERIALS AND METHODS: Metrics development went through iterative stages, including literature review, metrics and instrument development, internal and external testing and validation, and continuous revisions in each stage based on feedback. Furthermore, two experiments were conducted to determine brief and comprehensive versions of the instrument. RESULTS: The brief version of the instrument contained three dimensions: validity, significance, and feasibility. The comprehensive version of metrics included novelty, clinical relevance, potential benefits and risks, ethicality, testability, clarity, interestingness, and the three dimensions of the brief version. Each evaluation dimension included 2 to 5 subitems to evaluate the specific aspects of each dimension. For example, validity included clinical validity and scientific validity. The brief and comprehensive versions of the instruments included 12 and 39 subitems, respectively. Each subitem used a 5-point Likert scale. CONCLUSION: The validated brief and comprehensive versions of metrics can provide standardized, consistent, and generic measurements for clinical research hypotheses, allow clinical researchers to prioritize their research ideas systematically, objectively, and consistently, and can be used as a tool for quality assessment during the peer review process.
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spelling pubmed-98824462023-01-28 Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses Jing, Xia Zhou, Yuchun Cimino, James J. Shubrook, Jay H. Patel, Vimla L. De Lacalle, Sonsoles Weaver, Aneesa Liu, Chang medRxiv Article OBJECTIVES: Metrics and instruments can provide guidance for clinical researchers to assess their potential research projects at an early stage before significant investment. Furthermore, metrics can also provide structured criteria for peer reviewers to assess others’ clinical research manuscripts or grant proposals. This study aimed to develop, test, validate, and use evaluation metrics and instruments to accurately, consistently, and conveniently assess the quality of scientific hypotheses for clinical research projects. MATERIALS AND METHODS: Metrics development went through iterative stages, including literature review, metrics and instrument development, internal and external testing and validation, and continuous revisions in each stage based on feedback. Furthermore, two experiments were conducted to determine brief and comprehensive versions of the instrument. RESULTS: The brief version of the instrument contained three dimensions: validity, significance, and feasibility. The comprehensive version of metrics included novelty, clinical relevance, potential benefits and risks, ethicality, testability, clarity, interestingness, and the three dimensions of the brief version. Each evaluation dimension included 2 to 5 subitems to evaluate the specific aspects of each dimension. For example, validity included clinical validity and scientific validity. The brief and comprehensive versions of the instruments included 12 and 39 subitems, respectively. Each subitem used a 5-point Likert scale. CONCLUSION: The validated brief and comprehensive versions of metrics can provide standardized, consistent, and generic measurements for clinical research hypotheses, allow clinical researchers to prioritize their research ideas systematically, objectively, and consistently, and can be used as a tool for quality assessment during the peer review process. Cold Spring Harbor Laboratory 2023-05-26 /pmc/articles/PMC9882446/ /pubmed/36711561 http://dx.doi.org/10.1101/2023.01.17.23284666 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Jing, Xia
Zhou, Yuchun
Cimino, James J.
Shubrook, Jay H.
Patel, Vimla L.
De Lacalle, Sonsoles
Weaver, Aneesa
Liu, Chang
Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title_full Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title_fullStr Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title_full_unstemmed Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title_short Development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
title_sort development, validation, and usage of metrics to evaluate the quality of clinical research hypotheses
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882446/
https://www.ncbi.nlm.nih.gov/pubmed/36711561
http://dx.doi.org/10.1101/2023.01.17.23284666
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