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A SAS macro for modelling periodic data using cosinor analysis.

Margaret M Doyle1, Terrence E Murphy1, Margaret A Pisani2

  • 1Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, 300 George Street Suite 775, New Haven, CT, United States.

Computer Methods and Programs in Biomedicine
|August 11, 2021
PubMed
Summary
This summary is machine-generated.

New SAS macros simplify cosinor analysis for biological rhythm data, offering parameter estimation and visualization. This tool aids researchers in analyzing periodic data, such as circadian rhythms in critically ill patients.

Keywords:
AcrophaseCircadianCosinorMesorNadirRSAS

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Area of Science:

  • Chronobiology and Circadian Rhythms
  • Biostatistics and Data Analysis
  • Medical Informatics

Background:

  • Cosinor analysis, developed in the 1960s, is a statistical method for analyzing data with a known period, commonly applied to biological rhythms.
  • Existing software for cosinor analysis is available, but there is a lack of published SAS procedures or macros to facilitate these analyses.
  • This gap limits the accessibility and application of cosinor analysis within the SAS statistical environment.

Purpose of the Study:

  • To address the need for accessible cosinor analysis tools within SAS.
  • To introduce novel SAS macros designed for performing cosinor analyses on periodic biological data.
  • To provide researchers with a user-friendly method for analyzing biological rhythm data.

Main Methods:

  • Developed SAS macros capable of performing cosinor analyses for data with fixed periods and either normal or gamma distributions.
  • The macros generate datasets containing key cosinor parameters: acrophase, mesor, amplitude, and nadir, and include tests for rhythmicity.
  • The macros also output datasets with observed and model-fitted values and generate plots of the fitted cosine curve against the underlying data.

Main Results:

  • The developed SAS macros effectively perform cosinor analysis, providing comprehensive parameter estimates and visualizations.
  • Demonstrated the utility of the macros using real-world data on the circadian rhythms of heart rate and sleep in critically ill patients.
  • The analysis successfully characterized the periodic nature of physiological data in a clinical setting.

Conclusions:

  • Cosinor analysis offers a concise and understandable method for summarizing periodic data.
  • The newly developed SAS macros enhance the accessibility of cosinor analysis for a broader range of researchers.
  • These tools are expected to facilitate the wider adoption and application of cosinor analysis in biological and medical research.