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缺乏强大统计学背景的医学研究人员的回归建模教育和实践指南的系统评价:研究方案。

Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol.

机构信息

Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.

Berlin Institute of Health (BIH), Berlin, Germany.

出版信息

PLoS One. 2020 Dec 21;15(12):e0241427. doi: 10.1371/journal.pone.0241427. eCollection 2020.

Abstract

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.

摘要

在过去的几十年中,统计方法学得到了迅速发展,特别是在回归建模领域。多元回归模型几乎应用于所有医学研究项目中。因此,该领域中统计误解的潜在影响可能是巨大的。实际上,当前的理论统计知识并不总是充分应用于医学统计的当前实践中。一些医学期刊已经认识到这个问题,并发表了孤立的统计文章,甚至是整个系列。在本系统评价中,我们旨在评估通过医学期刊中发表的一系列统计文章向医学研究人员提供的回归建模的当前教育水平。本文是对系统评价的方案,旨在评估旨在培训和指导具有有限统计知识的应用医学研究人员的医学期刊中发表的统计系列涵盖了回归建模的哪些方面。统计论文系列不能像 Scopus 这样的电子搜索引擎中通过常见关键字轻松进行总结和识别。因此,我们通过向 STRATOS 倡议(加强观察性研究的分析思维)的一部分或相关的统计专家提出系统请求来确定系列。在每个已确定的文章中,两名评分者将独立检查文章的内容,以了解与回归建模相关的预定义关键方面列表。将使用预定义的报告表对主题相关文章的内容进行分析,以尽可能客观地评估内容。任何争议将由第三位评审员解决。摘要分析将确定可能对医学研究分析质量产生重要影响的潜在方法学差距和误解。因此,本综述将为回归建模领域的未来指导文件和教程提供基础,使医学研究人员能够:1)正确解读出版物,2)正确进行基本统计分析,3)识别需要统计专家帮助的情况。

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引用本文的文献

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Review of guidance papers on regression modeling in statistical series of medical journals.
PLoS One. 2022 Jan 24;17(1):e0262918. doi: 10.1371/journal.pone.0262918. eCollection 2022.

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