Psychiatry and Behavioral Sciences, New York Medical College, Valhalla, NY, USA.
Int J Clin Pract. 2013 May;67(5):407-11. doi: 10.1111/ijcp.12142.
Although great effort is made in clinical trials to demonstrate statistical superiority of one intervention vs. another, insufficient attention is paid regarding the clinical relevance or clinical significance of the observed outcomes. Effect sizes are not always reported. Available absolute effect size measures include Cohen's d, area under the curve, success rate difference, attributable risk and number needed to treat (NNT). Of all of these measures, NNT is arguably the most clinically intuitive and helps relate effect size difference back to real-world concerns of clinical practice. This commentary reviews the formula for NNT, and proposes acceptable values for NNT and its analogue, number needed to harm (NNH), using examples from the medical literature. The concept of likelihood to be helped or harmed (LHH), calculated as the ratio of NNH to NNT, is used to illustrate trade-offs between benefits and harms. Additional considerations in interpreting NNT are discussed, including the importance of defining acceptable response, adverse outcomes of interest, the effect of time, and the importance of individual baseline characteristics.
尽管在临床试验中付出了巨大努力来证明一种干预措施相对于另一种干预措施的统计学优势，但对于观察结果的临床相关性或临床意义关注不够。效果大小并不总是报告。可用的绝对效果大小度量包括 Cohen's d、曲线下面积、成功率差异、归因风险和需要治疗的人数（NNT）。在所有这些措施中，NNT 可以说是最具临床直观性的，有助于将效果大小差异与临床实践的实际问题联系起来。本评论回顾了 NNT 的公式，并使用医学文献中的示例提出了可接受的 NNT 值及其类似物，即需要伤害的人数（NNH）。帮助或伤害的可能性（LHH）的概念，即 NNH 与 NNT 的比值，用于说明益处和危害之间的权衡。讨论了在解释 NNT 时需要考虑的其他因素，包括定义可接受的反应、感兴趣的不良结局、时间的影响以及个体基线特征的重要性。
