Proportional constrained longitudinal data analysis models for clinical trials in sporadic Alzheimer's disease

Abstract

Abstract Introduction Clinical trials for sporadic Alzheimer's disease generally use mixed models for repeated measures (MMRM) or, to a lesser degree, constrained longitudinal data analysis models (cLDA) as the analysis model with time since baseline as a categorical variable. Inferences using MMRM/cLDA focus on the between‐group contrast at the pre‐determined, end‐of‐study assessments, thus are less efficient (eg, less power). Methods The proportional cLDA (PcLDA) and proportional MMRM (pMMRM) with time as a categorical variable are proposed to use all the post‐baseline data without the linearity assumption on disease progression. Results Compared with the traditional cLDA/MMRM models, PcLDA or pMMRM lead to greater gain in power (up to 20% to 30%) while maintaining type I error control. Discussion The PcLDA framework offers a variety of possibilities to model longitudinal data such as proportional MMRM (pMMRM) and two‐part pMMRM which can model heterogeneous cohorts more efficiently and model co‐primary endpoints simultaneously.

Keywords

• Alzheimer's disease
• proportional constrained longitudinal data analysis model (PcLDA)
• MMRM
• proportional MMRM (pMMRM)
• proportional treatment effect