Mixed model of repeated measures versus slope models in Alzheimer’s disease clinical trials
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Randomized clinical trials of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) typically assess intervention efficacy with measures of cognitive or functional assessments repeated every six months for one to two years. The Mixed Model of Repeated Measures (MMRM), which assumes an “unstructured mean” by treating time as categorical, is attractive because it makes no assumptions about the shape of the mean trajectory of the outcome over time. However, categorical time models may be over-parameterized and inefficient in detecting treatment effects relative to continuous time models of, say, the linear trend of the outcome over time. Mixed effects models can also be extended to model quadratic time effects, although it is questionable whether the duration and interval of observations in AD and MCI studies is sufficient to support such models. Furthermore, it is unknown which of these models are most robust to missing data, which plagues AD and MCI studies. We review the literature and compare estimates of treatment effects from four potential models fit to data from five AD Cooperative Study (ADCS) trials in MCI and AD.
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- Mixed model of repeated measures versus slope models in Alzheimer’s disease clinical trials
The journal of nutrition, health & aging
Volume 16, Issue 4 , pp 360-364
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Clinical trials efficiency
- mixed effects models
- mixed model of repeated measures (MMRM)
- quantitative review
- missing data
- Industry Sectors
- Author Affiliations
- 1. Department of Family & Preventive Medicine, Division of Biostatistics & Bioinformatics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0949, USA
- 2. Department of Neurosciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0949, USA