Enhancing cosinor analysis of circadian phase markers using the gamma distribution

Margaret M. Doyle, Terrence E. Murphy, Brienne Miner, Margaret A. Pisani, Elizabeth R. Lusczek, Melissa P. Knauert

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The cosinor model, in which a cosine curve is fitted to periodic data within a regression model, is a frequently used method for describing patterns of cyclical activity such as circadian rhythms. For circadian variables of interest (eg, melatonin and heart rate) that do not take on negative values, the assumption of normally distributed residuals required by the general linear model, which is most commonly used for cosinor analysis, may not be appropriate. Alternatively, a generalized linear model with the gamma distribution (GZLM-gamma) is specifically defined to accommodate non-negative outcomes. Herein, we demonstrate the improved fit and gains of efficiency in detection of circadian rhythm afforded by using the GZLM-gamma in cosinor models of heart rate, actigraphic activity, and urinary 6-sulfatoxymelatonin. Notably, this improved detection of circadian rhythm allows retention of additional patients for downstream analyses, further improving study power.

Original languageEnglish (US)
Pages (from-to)1-3
Number of pages3
JournalSleep Medicine
Volume92
DOIs
StatePublished - Apr 2022

Bibliographical note

Funding Information:
Ms. Doyle and Dr. Murphy were supported by the Yale Claude D. Pepper Older Americans Independence Center ( P30AG021342 ). Dr. Pisani was supported by the NINR ( R21 NR011066 ). Dr. Knauert was supported by the NHLBI ( K23 HL138229 ), the Academy of Sleep Medicine Foundation, and the Fund to Retain Clinical Scientists at Yale sponsored by the Doris Duke Charitable Foundation award #2015216 and the Yale Center for Clinical Investigation.

Publisher Copyright:
© 2022

Keywords

  • Actigraphy
  • Circadian
  • Cosinor
  • Diurnal variation
  • Gamma distribution
  • Normal distribution

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