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An example of using mixed models and PROC MIXED for longitudinal data

R D Wolfinger1

  • 1SAS Institute Inc., Cary, North Carolina 27513, USA.

Journal of Biopharmaceutical Statistics
|November 14, 1997
PubMed
Summary
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This study explores general linear mixed models for analyzing longitudinal data, which involves repeated measurements over time. These statistical methods help draw meaningful inferences from complex datasets, demonstrated with blood pressure examples.

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Longitudinal data, comprising repeated measurements on subjects over time, are frequently encountered in biostatistical research.
  • Analyzing such data requires specialized statistical techniques to yield valid inferences.

Purpose of the Study:

  • To discuss common general linear mixed models for analyzing longitudinal data.
  • To illustrate the application of these models using a blood pressure repeated measures example.

Main Methods:

  • The study focuses on general linear mixed models.
  • Statistical analysis is performed using the MIXED procedure in the SAS System.
  • Code descriptions and output interpretations are provided for practical implementation.

Main Results:

Related Experiment Videos

  • The paper demonstrates fitting common mixed models to a prototypical longitudinal dataset.
  • The example involves analyzing repeated blood pressure measurements.

Conclusions:

  • General linear mixed models are effective tools for analyzing longitudinal data in biostatistics.
  • Practical implementation using SAS is facilitated through provided code and interpretation guidance.