Optimal Partitioning for Linear Mixed Effects Models: Applications to Identifying Placebo Responders

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A long--standing problem in clinical research is distinguishing drug treated subjects that respond due to specific effects of the drug from those that respond to non-specific (or placebo) effects of the treatment. Linear mixed effect models are commonly used to model longitudinal clinical trial data. A solution to this problem is presented using an optimal partitioning methodology for linear mixed effects models. The approach is compared and contrasted with a growth mixture model approach. The methodology is applied to a twophase depression clinical trial where subjects in a first phase were treated openly for 12 weeks with fluoxetine followed by a double blind discontinuation phase where responders to treatment in the first phase were randomized to either stay on fluoxetine or switched to a placebo.


This paper was presented at the International Chinese Statistical Association Year 2010 Applied Statistics Symposium, June 20-23, 2010, Indianapolis, IN.