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Advanced Analysis of Variance, Random Effects and Mixed Effects Models

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
Chapter

Abstract

In clinical trials it is common to assume a fixed effects research model. This means that the patients selected for a specific treatment are assumed to be homogeneous and have the same true quantitative effect and that the differences observed are residual, meaning that they are caused by inherent variability in biological processes, rather than some hidden subgroup property. If, however, we have reasons to believe that certain patients due to co-morbidity, co-medication, age or other factors will respond differently from others, then the spread in the data is caused not only by the residual effect but also by between patient differences due to some subgroup property. It may even be safe to routinely treat any patient effect as a random effect, unless there are good arguments no to do so. Random effects research models require a statistical approach different from that of fixed effects models (Anonymous 2006; Campbell 2006; Gao 2003).

Keywords

Health Center Treatment Efficacy Random Effect Model Fixed Effect Model Residual Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Ton J. Cleophas
    • 1
    • 2
  • Aeilko H. Zwinderman
    • 1
    • 3
  1. 1.Applied to Clinical TrialsEuropean Interuniversity College of Pharmaceutical MedicineLyonFrance
  2. 2.Department of MedicineAlbert Schweitzer HospitalDordrechtNetherlands
  3. 3.Department of Biostatistics and EpidemiologyAcademic Medical CenterAmsterdamNetherlands

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