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A Guide to Reproducibility in Preclinical Research.
Samsa, Greg; Samsa, Leigh.
Afiliação
  • Samsa G; G. Samsa is associate professor, Department of Biostatistics and Bioinformatics, and director, Research Integrity Office, Duke University School of Medicine, Durham, North Carolina. L. Samsa is postdoctoral teaching scholar, BIT Biotechnology Program, North Carolina State University, Raleigh, North Carolina.
Acad Med ; 94(1): 47-52, 2019 01.
Article em En | MEDLINE | ID: mdl-29995667
Many have raised concerns about the reproducibility of biomedical research. In this Perspective, the authors address this "reproducibility crisis" by distilling discussions around reproducibility into a simple guide to facilitate understanding of the topic.Reproducibility applies both within and across studies. The following questions address reproducibility within studies: "Within a study, if the investigator repeats the data management and analysis, will she get an identical answer?" and "Within a study, if someone else starts with the same raw data, will she draw a similar conclusion?" Contrastingly, the following questions address reproducibility across studies: "If someone else tries to repeat an experiment as exactly as possible, will she draw a similar conclusion?" and "If someone else tries to perform a similar study, will she draw a similar conclusion?"Many elements of reproducibility from clinical trials can be applied to preclinical research (e.g., changing the culture of preclinical research to focus more on transparency and rigor). For investigators, steps toward improving reproducibility include specifying data analysis plans ahead of time to decrease selective reporting; more explicit data management and analysis protocols; and increasingly detailed experimental protocols, which allow others to repeat experiments. Additionally, senior investigators should take greater ownership of the details of their research (e.g., implementing active laboratory management practices, such as random audits of raw data [or at least reduced reliance on data summaries], more hands-on time overseeing experiments, and encouraging a healthy skepticism from all contributors). These actions will support a culture where rigor + transparency = reproducibility.
Assuntos

Texto completo: 1 Eixos temáticos: Pesquisa_clinica Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Ensaios Clínicos como Assunto / Guias como Assunto / Pesquisa Biomédica Tipo de estudo: Guideline / Qualitative_research Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Eixos temáticos: Pesquisa_clinica Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa / Ensaios Clínicos como Assunto / Guias como Assunto / Pesquisa Biomédica Tipo de estudo: Guideline / Qualitative_research Limite: Humans País como assunto: America do norte Idioma: En Ano de publicação: 2019 Tipo de documento: Article