Nonlinear longitudinal proportional effect models have been proposed to improve power and provide direct estimates of the proportional treatment effect in randomized clinical trials. These models assume a fixed proportional treatment effect over time, which can lead to bias and Type I error inflation when the assumption is violated. Even when the proportional effect assumption holds, these models are biased, and their inference is sensitive to the labeling of treatment groups. Typically, this bias favors the active group, inflates Type I error, and can result in one-sided testing. Conversely, the bias can make it more difficult to detect treatment harm, creating a safety concern.

翻译：非线性纵向比例效应模型被提出用于提高随机临床试验的统计功效并提供治疗比例效应的直接估计。这些模型假设治疗比例效应随时间保持恒定，当该假设不成立时，可能导致偏倚和第一类错误膨胀。即使比例效应假设成立，这些模型也存在偏倚，且其统计推断对治疗组标签设定敏感。通常，这种偏倚会偏向活性治疗组，导致第一类错误膨胀，并可能产生单侧检验倾向。反之，这种偏倚也可能使检测治疗危害更为困难，从而引发安全性担忧。