Repeated Measures Model

Part of the Springer Texts in Statistics book series (STS)


In contrast to the previous chapters, we now assume that instead of having only one observation per object/subject (e.g., patient) we now have repeated observations. These repeated measurements are collected at previously exact defined times. The principle idea is that these observations give information about the development of a response Y . This response might, for instance, be the blood pressure (measured every hour) for a fixed therapy (treatment A), the blood sugar level (measured every day of the week), or the monthly training performance of sprinters for training method A, etc., i.e., variables which change with time (or a different scale of measurement). The aim of a design like this is not so much the description of the average behavior of a group (with a fixed treatment), rather the comparison of two or more treatments and their effect across the scale of measurement (e.g., time), i.e., the treatment or therapy comparison.

First of all, before we deal with this interesting question, let us introduce the model for one treatment, i.e., for one sample from one population.


Covariance Matrix Measure Model Markov Chain Model Observation Vector Loglinear Model 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Institut für StatistikLudwig-Maximilians-UniversitätMünchenGermany
  2. 2.Department of Mathematics & StatisticsIndian Institute of TechnologyKanpurIndia

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