General Approaches to Analysis of Course: Applying Growth Mixture Modeling to Randomized Trials of Depression Medication

نویسندگان

  • Bengt Muthén
  • Hendricks Brown
  • Andrew Leuchter
  • Aimee Hunter
چکیده

This chapter discusses the use of growth mixture modeling to assess treatment effects in clinical trials. The motivation is a study of depression medication in a double-blind placebo-controlled trial. Studies of this type typically show placebo response and placebo non response. Growth mixture modeling (GMM) is well suited for representing such heterogeneity among subjects in that it can identify different types of trajectory shapes. GMM can be seen as a combination of conventional mixed effects modeling and cluster analysis, also allowing prediction of class membership and estimation of each individual’s most likely class membership. GMM has particularly strong potential for analyses of randomized trials because it responds to the need to investigate for whom a treatment is effective by allowing for different treatment effects in different trajectory classes. In this trial, a separate analysis of the placebo group finds evidence of a placebo response trajectory class with a strong initial improvement, followed by a later worsening. A separate analysis of the medication group shows two types of responder classes, one with an initial improvement only and one with a sustained improvement. A joint analysis of the placebo and medication groups makes it possible to estimate medication effects in the presence of placebo-response effects and shows benefits of medication. Analysis strategies and alternatives for assessing medication effects are discussed.

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تاریخ انتشار 2008