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A SAS macro for multilevel Cosinor analysis.

Margaret M Doyle1, Terrence E Murphy2, Brienne Miner1

  • 1Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.

Computer Methods and Programs in Biomedicine
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Multilevel cosinor analysis offers a more convenient and accurate method for analyzing periodic data compared to traditional two-stage approaches. Developed SAS macros facilitate this advanced technique, improving model fit for individual data.

Keywords:
AcrophaseCircadianCosinorGrowth modelsMesorMultilevelNadirRSAS

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Area of Science:

  • Biostatistics
  • Chronobiology
  • Statistical Modeling

Background:

  • Cosinor analysis models cyclical variations in periodic data, providing estimates like MESOR, amplitude, and acrophase.
  • Traditional two-stage cosinor analysis fits curves individually, with downstream comparisons.
  • Multilevel cosinor modeling allows simultaneous analysis of multiple individuals, potentially improving model complexity and individual data fit.

Purpose of the Study:

  • Introduce multilevel cosinor models and accompanying SAS macros.
  • Provide tools for researchers to apply advanced cosinor analysis.
  • Compare the model fit of multilevel versus two-stage cosinor methods.

Main Methods:

  • Developed SAS macros for multilevel cosinor analysis.
  • Included features for selecting random variable specifications and adding grouping variables.
  • Performed cross-validation analyses to compare multilevel and single-level (two-stage) approaches.

Main Results:

  • The developed SAS macros facilitate model building and selection for multilevel cosinor analyses.
  • Parameter estimates, model fit measures, and graphical outputs aid in understanding model appropriateness.
  • Cross-validation demonstrated superior model fit for the multilevel approach compared to the single-level method.

Conclusions:

  • Multilevel cosinor analysis enhances the single-subject cosinor model with easier model selection.
  • This approach may offer improved data fit for individual subjects.
  • Encourages wider adoption of multilevel cosinor analysis in research.