Traditionally, studies in experimental physiology have been conducted in small groups of human participants, animal models or cell lines. Identifying optimal study designs that achieve sufficient power for drawing proper statistical inferences to detect group level effects with small sample sizes has been challenging. Moreover, average effects derived from traditional group-level inference do not necessarily apply to individual participants. Here, we introduce N-of-1 trials as an innovative study design that can be used to draw valid statistical inference about the effects of interventions on individual participants and can be aggregated across multiple study participants to provide population-level inferences more efficiently than standard group randomized trials. N-of-1 trials have been used since the late 1980s, but without large-scale adoption and with few applications in experimental physiology research settings. In this manuscript, we introduce the key components and design features of N-of-1 trials, describe statistical analysis and interpretations of the results, and describe some available digital tools to facilitate their use using examples from experimental physiology.
翻译:传统上,实验生理学研究通常在小规模人类参与者群体、动物模型或细胞系中进行。在样本量较小的情况下,如何确定能够获得足够统计效力以进行适当统计推断、从而检测群体水平效应的最优研究设计一直具有挑战性。此外,从传统群体水平推断得出的平均效应未必适用于个体参与者。本文介绍N-of-1试验作为一种创新研究设计,可用于对干预措施在个体参与者身上的效应进行有效的统计推断,并且可以跨多个研究参与者进行聚合,从而比标准的群体随机试验更高效地提供群体水平推断。N-of-1试验自20世纪80年代末开始使用,但尚未得到大规模采纳,在实验生理学研究环境中的应用也较少。在本手稿中,我们介绍了N-of-1试验的关键组成部分和设计特点,描述了结果的统计分析和解读,并通过实验生理学中的实例说明了一些可用的数字工具以促进其应用。