优化中医药针灸研究统计方法学:提升透明度与分析精度OA

Background:Traditional Chinese Medicine(TCM),with over 5000 years of empirical practice,increasingly employs modern scientific frameworks such as randomized controlled trials(RCTs)to validate therapeutic claims,yet its research reliability hinges critically on robust statistical rigor. Methods:By systematically analyzing articles from Phytomedicine and Journal of Ethnopharmacology,this study evaluates statistical methodologies in TCM research,focusing on the adoption of advanced analyti-cal techniques(e.g.,multivariate modeling)versus reliance on basic methods(e.g.,ANOVA)and identifies reporting gaps in trial design(e.g.,sample size estimation). Results:Key findings indicate that foundational statistical methods,such as one-way ANOVA,were pre-dominantly used(83.4％of articles),whereas more advanced approaches appeared in only 34.6％of stud-ies.However,methodological rigor should not be equated with statistical complexity.The selection of analytical techniques must be driven by the research objectives,data structure,study design,and the complexity of the scientific questions under investigation.Advanced methods are not inherently supe-rior;rather,the most appropriate approach is the one that is methodologically justified and aligned with underlying assumptions.Notably,substantial deficiencies in trial design and reporting were observed.A striking 81.5％of studies lacked pre-specified power calculations or sample size justifications,raising con-cerns about statistical validity.Reporting transparency was similarly limited:48.3％of articles did not adequately describe statistical procedures,and 69.8％failed to provide confidence intervals for primary effect estimates.Collectively,these limitations increase the risk of biased interpretation and undermine the robustness,reproducibility,and credibility of the findings. Conclusion:Strengthening statistical rigor-through improved trial design transparency and adoption of advanced methods-is essential to enhance the credibility of TCM research,mitigate biases,and foster its integration into evidence-based medicine,ultimately ensuring clinically meaningful and actionable therapeutic insights.