Natural cubic splines for the analysis of Alzheimer's clinical trials

TL;DR

This paper proposes a natural cubic spline approach for analyzing Alzheimer's clinical trial data, addressing limitations of traditional categorical-time models like MMRM, especially with irregular visit schedules.

Contribution

It introduces a spline-based model for continuous time analysis in clinical trials, improving robustness against off-schedule assessments and delays.

Findings

Spline model outperforms MMRM in simulations

Better handling of off-schedule visits

Reduces bias from delayed assessments

Abstract

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.