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. 2023 May-Jun;22(3):508-519.
doi: 10.1002/pst.2285. Epub 2023 Jan 10.

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

Michael C Donohue  1 , Oliver Langford  1 , Philip S Insel  2 , Christopher H van Dyck  3 , Ronald C Petersen  4 , Suzanne Craft  5 , Gopalan Sethuraman  1 , Rema Raman  1 , Paul S Aisen  1 ; Alzheimer's Disease Neuroimaging Initiative
Affiliations

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

Michael C Donohue et al. Pharm Stat. 2023 May-Jun.

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 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. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.

Trial registration: ClinicalTrials.gov NCT02167256 NCT01767909 NCT00000173.

Keywords: DPM; MMRM; cLDA; constrained longitudinal data analysis; disease progression models; mixed model repeated measures; natural cubic splines.

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Figures

FIGURE 1
FIGURE 1
Modeled mean ADAS-Cog for each study. All models assume unstructured variance-covariance. ADAS-Cog, Alzheimer’s Disease Assessment Scale–Cognitive Subscale; MCI, Mild Cognitive Impairment
FIGURE 2
FIGURE 2
Modeled treatment group contrast in ADAS-Cog for each study. All models assume unstructured variance-covariance. ADAS-Cog, Alzheimer’s Disease Assessment Scale–Cognitive Subscale; MCI, Mild Cognitive Impairment
See this image and copyright information in PMC

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