健康研究人员的线性回归报告实践,一项横断面元研究。

IF 2.9 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-03-20 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0305150
Lee Jones, Adrian Barnett, Dimitrios Vagenas
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引用次数: 0

摘要

背景:有关医疗保健的决策,例如新的疾病治疗方法是否有效,通常都是根据已发表论文中的证据做出的。然而,对统计方法和结果的报告不力是健康研究中普遍存在的问题,这有可能导致临床实践中使用无效或有害的治疗方法。统计建模的选择往往会极大地影响结果。作者并不总能提供足够的信息来评估和重复他们的方法,这就给解释结果带来了困难。我们的研究旨在了解当前的报告实践,并为教育研究人员提供参考:我们在2019年从PLOS ONE随机抽取的95篇健康领域发表的论文中评估了线性回归的报告实践,这些论文被随机分配给统计学家进行发表后审查。使用频率、百分比和威尔逊95%置信区间描述了报告方法的普遍性:92%的作者报告了P值,81%的作者报告了回归系数,但只有58%的论文报告了不确定性度量,如置信区间或标准误差。69%的作者没有讨论估计值的科学重要性,只有23%的作者直接解释了系数的大小:我们的研究结果表明,统计方法和结果的报告往往不够详尽,无法重现。为了提高统计质量,将医疗资金用于有效治疗,我们建议统计人员参与从研究设计到同行评审后的研究周期。研究环境是一个生态系统,未来解决统计质量低下问题的干预措施应考虑个人、组织和政策环境之间的相互作用。实用建议包括:期刊制作标准化报告模板,使用交互式核对表改进报告实践。需要对研究维护和质量控制进行投资,以评估和实施这些建议,提高健康研究的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linear regression reporting practices for health researchers, a cross-sectional meta-research study.

Background: Decisions about health care, such as the effectiveness of new treatments for disease, are regularly made based on evidence from published work. However, poor reporting of statistical methods and results is endemic across health research and risks ineffective or harmful treatments being used in clinical practice. Statistical modelling choices often greatly influence the results. Authors do not always provide enough information to evaluate and repeat their methods, making interpreting results difficult. Our research is designed to understand current reporting practices and inform efforts to educate researchers.

Methods: Reporting practices for linear regression were assessed in 95 randomly sampled published papers in the health field from PLOS ONE in 2019, which were randomly allocated to statisticians for post-publication review. The prevalence of reporting practices is described using frequencies, percentages, and Wilson 95% confidence intervals.

Results: While 92% of authors reported p-values and 81% reported regression coefficients, only 58% of papers reported a measure of uncertainty, such as confidence intervals or standard errors. Sixty-nine percent of authors did not discuss the scientific importance of estimates, and only 23% directly interpreted the size of coefficients.

Conclusion: Our results indicate that statistical methods and results were often poorly reported without sufficient detail to reproduce them. To improve statistical quality and direct health funding to effective treatments, we recommend that statisticians be involved in the research cycle, from study design to post-peer review. The research environment is an ecosystem, and future interventions addressing poor statistical quality should consider the interactions between the individuals, organisations and policy environments. Practical recommendations include journals producing templates with standardised reporting and using interactive checklists to improve reporting practices. Investments in research maintenance and quality control are required to assess and implement these recommendations to improve the quality of health research.

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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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