  Mixed model repeated measures (MMRM) is the most common analysis approach
used in clinical trials for Alzheimer's disease and other progressive diseases
measured with continuous outcomes measured over time. The model treats time as
a categorical variable, which allows an unconstrained estimate of the mean for
each study visit in each randomized group. Categorizing time in this way can be
problematic when assessments occur off-schedule, as including off-schedule
visits can induce bias, and excluding them ignores valuable information and
violates the intention to treat principle. This problem has been exacerbated by
clinical trial visits which have been delayed due to the COVID19 pandemic. As
an alternative to MMRM, we propose a constrained longitudinal data analysis
with natural cubic splines that treats time as continuous and uses test version
effects to model the mean over time. The spline model is shown to be superior,
in practice and simulation studies, to categorical-time models like MMRM and
models that assume a proportional treatment effect.
