A critical evaluation of longitudinal proportional effect models

Nonlinear longitudinal models for repeated continuous measures with proportional treatment effects have been proposed to improve power and provide direct estimates of the proportional treatment effect in randomized clinical trials. These models make a strong assumption about 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, we demonstrate that 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.