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Background <p>Statistical methods are fundamental to the design, analysis, and interpretation of surgical research. Despite their centrality, many clinicians—particularly surgeons—receive limited formal training in statistics, often leading to misinterpretation of data and overreliance on simplified metrics such as <i>p</i>-values. This review provides an in-depth clinically oriented guide to key statistical techniques used in surgical studies, offering clarity on their appropriate use, interpretation, and limitations.</p> Methods <p>We systematically present and explain commonly used statistical methods in surgical research, including descriptive statistics, chi-square and Fisher’s exact tests, logistic and Cox regression models, Kaplan–Meier survival analysis, and propensity score techniques. Special emphasis is placed on the correct interpretation of diagnostic metrics (sensitivity, specificity, and predictive values), confidence intervals, and effect sizes. A&#xa0;dedicated section addresses widespread misconceptions about statistical significance and the limitations of <i>p</i>-values, offering alternative approaches such as Bayesian inference and confidence-based interpretation.</p> Results <p>By demystifying statistical tools and emphasizing clinical relevance over arbitrary thresholds of significance, this review bridges the gap between the theory of biostatistics and surgical practice. A&#xa0;fictional Kaplan–Meier analysis illustrates how survival data can be visualized and interpreted effectively. Challenges in surgical research, including bias, confounding, missing data, and issues of generalizability, are discussed, with practical recommendations.</p> Conclusion <p>Robust statistical literacy is essential for interpreting and conducting high-quality surgical research. This review provides a&#xa0;practical, surgeon-oriented framework for understanding statistical concepts, avoiding common pitfalls, and adopting best practices in evidence appraisal. Enhancing statistical competence among surgical clinicians will improve both the quality of research and its translation into improved patient outcomes.</p>

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Understanding and applying statistical methods in surgical research: a comprehensive guide for clinicians

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Jasmin Zeindler,
  • Anas Taha,
  • Florian Ponholzer,
  • Vincent Ochs,
  • Khoshimov Rakhmatillokhon,
  • Savas Soysal,
  • Otto Kollmar,
  • Robert Rosenberg

摘要

Background

Statistical methods are fundamental to the design, analysis, and interpretation of surgical research. Despite their centrality, many clinicians—particularly surgeons—receive limited formal training in statistics, often leading to misinterpretation of data and overreliance on simplified metrics such as p-values. This review provides an in-depth clinically oriented guide to key statistical techniques used in surgical studies, offering clarity on their appropriate use, interpretation, and limitations.

Methods

We systematically present and explain commonly used statistical methods in surgical research, including descriptive statistics, chi-square and Fisher’s exact tests, logistic and Cox regression models, Kaplan–Meier survival analysis, and propensity score techniques. Special emphasis is placed on the correct interpretation of diagnostic metrics (sensitivity, specificity, and predictive values), confidence intervals, and effect sizes. A dedicated section addresses widespread misconceptions about statistical significance and the limitations of p-values, offering alternative approaches such as Bayesian inference and confidence-based interpretation.

Results

By demystifying statistical tools and emphasizing clinical relevance over arbitrary thresholds of significance, this review bridges the gap between the theory of biostatistics and surgical practice. A fictional Kaplan–Meier analysis illustrates how survival data can be visualized and interpreted effectively. Challenges in surgical research, including bias, confounding, missing data, and issues of generalizability, are discussed, with practical recommendations.

Conclusion

Robust statistical literacy is essential for interpreting and conducting high-quality surgical research. This review provides a practical, surgeon-oriented framework for understanding statistical concepts, avoiding common pitfalls, and adopting best practices in evidence appraisal. Enhancing statistical competence among surgical clinicians will improve both the quality of research and its translation into improved patient outcomes.