Abstract：
Clinical trials often demonstrate treatment efficacy through change in forced expiratory volume in one second (FEV1), comparing single FEV1 measurements from post- versus pre-treatment timepoints. Day-to-day variation in measured FEV1 is common for reasons such as diurnal variation and intermittent health changes, relative to a stable, monthly average. This variation can alter estimation of associations between change in FEV1 and baseline in predictable ways, through a phenomenon called regression to the mean. We quantify and explain day-to-day variation in percent-predicted FEV1 (ppFEV1) from 4 previous trials, and we present a statistical, data-driven explanation for potential bias in ceiling and floor effects due to commonly observed amounts of variation. We recommend accounting for variation when assessing associations between baseline value and change in CF outcomes in single-arm trials, and we consider possible impact of variation on conventional standards for study eligibility.
摘要：
临床试验通常通过一秒用力呼气量（FEV1）的变化来证明治疗效果。比较治疗后与治疗前时间点的单个FEV1测量值。由于昼夜变化和间歇性健康变化等原因，测量的FEV1的日常变化很常见，相对于稳定，月平均。这种变化可以以可预测的方式改变FEV1变化和基线之间的关联估计，通过一种叫做回归均值的现象。我们量化并解释了先前4项试验中预测的FEV1百分比（ppFEV1）的日常变化，我们提供了一个统计数据，由于通常观察到的变化量，对天花板和地板效应中潜在偏差的数据驱动解释。我们建议在单臂试验中评估基线值和CF结果变化之间的关联时考虑差异，我们考虑了变异对研究资格的常规标准的可能影响。
