Response to Letter to the Editor "Comments on 'Novel Non-Linear Models for Clinical Trial Analysis With Longitudinal Data: A Tutorial Using SAS for Both Frequentist and Bayesian Methods'".

Abstract

In clinical trials with longitudinal continuous data, efficacy inference traditionally focuses on the difference in the mean change from baseline at a single study visit [e.g., mixed models for repeated measures (MMRM)]. Proportional MMRM (pMMRM) reparameterizes this difference as a proportional reduction relative to the placebo mean change. This proportional effect is a nonlinear combination of the means, whereas the difference is a linear combination of the means. It can not only lead to greater power at a single visit by yielding a test statistic lower-bounded by that of the difference but also offers a flexible and intuitive way to combine all or multiple visits for efficacy inference, which can further boost power. It is also asymptotically unbiased. pMMRM with visit-specific proportional effects yields identical parameter estimates to MMRM. When only MMRM outputs are used, the proportional effect calculated by the delta method yields greater power than the difference.