Abstract: Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a “non-significant” p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

Keywords: statistics, statistical significance, effect size, p-value


中文翻译：

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老年医学和康复领域的频率论、贝叶斯分析和补充统计工具：传统的零假设显着性检验方法是否足够？

摘要：原假设显着性检验（NHST）是老年医学和康复领域的主要统计方法。然而，NHST 经常被误解或误用。在这种情况下，临床试验的结果将被视为无效的证据，而事实上，临床相关问题可能具有“不显着”的p值。相反，当组间观察到显着差异时，研究结果被认为具有临床相关性。为了假设p值不是关联或效应存在的唯一指标，应鼓励研究人员报告其他统计分析方法，如贝叶斯分析和与 p值一起的补充统计工具（例如，效应大小、置信度）间隔、最小临床重要差异和基于幅度的推断），通过提供更有效和更全面的分析来改善对临床试验结果的解释。然而，对贝叶斯分析和二次统计分析的关注并不意味着 NHST 不那么重要。只是，为了观察真正的干预效果，研究人员应该结合使用二次统计分析和 NHST 或贝叶斯统计分析来揭示老年和康复研究中p值无法显示的内容（例如，体重增加 1 公斤的临床重要性）与对照组相比，长寿老年人干预组的握力）。本文通过利用贝叶斯和二次统计分析来更好地审查临床试验的结果，为改善康复和老年医学领域科学数据的解释提供了潜在的见解，其中单独的p值可能不适合确定干预措施的功效。

关键词：统计、统计显着性、效应大小、p 值