Online Program

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Wednesday, September 27
Wed, Sep 27, 9:45 AM - 10:30 AM
TBD
Poster Session

Impact of Variance-Covariance Specification in MMRM on Multiple Comparisons Inference (300440)

Jason C Hsu, Eli Lilly and Company and The Ohio State University 
*meng li, Ohio State University  

Keywords: MMRM, Mixed effects model

Clinical trials in diabetes, schizophrenia, and Alzheimer’s Disease measure effects of treatments on each patient repeatedly at multiple time points. Such data are typically analyzed using a mixed effects model. Insight into the variance-covariance of the Repeated Measures is given by deriving and examining its structure for a simple Random coefficients response model. To isolate the consequence of specifying a variance-covariance structure far from the truth, we cast our investigation in a setting where the fixed treatment effect is orthogonal to the random effect and the interactions. By analyzing data sets satisfying such orthogonality condition under different variance-covariance types, we find the type specification itself has a significant impact on testing for treatment efficacy at multiple time points.