Patel Sharad, Green Adam
Department of Critical Care Medicine, Cooper University Health Care and Cooper Medical School of Rowan University, 1 Cooper Plaza, Camden, NJ, 08103, USA.
Crit Care. 2025 Feb 11;29(1):73. doi: 10.1186/s13054-025-05307-9.
The p-value has changed from a versatile tool for scientific reasoning to a strict judge of medical information, with the usual 0.05 cutoff frequently deciding a study's significance and subsequent clinical use. Through an examination of five critical care interventions that demonstrated meaningful treatment effects yet narrowly missed conventional statistical significance, this paper illustrates how rigid adherence to p-value thresholds may obscure therapeutically beneficial findings. By providing a clear, step-by-step illustration of a basic Bayesian calculation, we demonstrate that clinical importance can remain undetected when relying solely on p-values. These observations challenge current statistical paradigms and advocate for hybrid approaches-including both frequentist and Bayesian methodologies-to provide a more comprehensive understanding of clinical data, ultimately leading to better-informed medical decisions.
P值已从科学推理的通用工具转变为医学信息的严格评判标准,通常以0.05作为临界值,常常据此判定一项研究的意义及后续临床应用。通过对五项重症监护干预措施的考察,这些措施显示出有意义的治疗效果,但勉强未达到传统统计学显著性水平,本文阐明了严格遵循P值阈值可能会掩盖具有治疗益处的发现。通过清晰、逐步地展示基本贝叶斯计算方法,我们证明,仅依靠P值时,临床重要性可能会被忽视。这些观察结果对当前的统计范式提出了挑战,并倡导采用混合方法——包括频率学派和贝叶斯方法——以更全面地理解临床数据,最终做出更明智的医疗决策。