Jones Lee, Barnett Adrian, Vagenas Dimitrios
Research Methods Group, Faculty of Health, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
AusHSI, Centre for Healthcare Transformation, Faculty of Health, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Queensland, Australia.
PLoS One. 2025 Mar 20;20(3):e0305150. doi: 10.1371/journal.pone.0305150. eCollection 2025.
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.
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.
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.
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.
关于医疗保健的决策，例如新疾病治疗方法的有效性，通常是基于已发表研究的证据做出的。然而，健康研究中普遍存在统计方法和结果报告不佳的问题，这可能导致临床实践中使用无效或有害的治疗方法。统计建模选择往往对结果有很大影响。作者并不总是提供足够的信息来评估和重复他们的方法，这使得结果解释变得困难。我们的研究旨在了解当前的报告做法，并为教育研究人员的工作提供信息。
对2019年从《公共科学图书馆·综合》中随机抽取的95篇健康领域已发表论文中的线性回归报告做法进行评估，这些论文被随机分配给统计学家进行发表后审查。报告做法的流行程度通过频率、百分比和威尔逊95%置信区间来描述。
虽然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。
我们的结果表明，统计方法和结果的报告往往很差，没有足够的细节来重现它们。为了提高统计质量并将卫生资金导向有效的治疗方法，我们建议统计学家参与从研究设计到同行评审后的整个研究周期。研究环境是一个生态系统，未来解决统计质量差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。实际建议包括期刊制作具有标准化报告的模板，并使用交互式清单来改进报告做法。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，从而提高健康研究的质量。
