Margaret M Doyle1, Terrence E Murphy2, Margaret A Pisani3, Henry K Yaggi3, Sangchoon Jeon4, Nancy S Redeker5, Melissa P Knauert3. 1. Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, 300 George Street Suite 775, New Haven, CT, United States. Electronic address: Margaret.Doyle@yale.edu. 2. Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, 300 George Street Suite 775, New Haven, CT, United States. 3. Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States. 4. Yale School of Nursing, West Haven, CT, United States. 5. Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States; Yale School of Nursing, West Haven, CT, United States.
Abstract
BACKGROUND AND OBJECTIVE: Cosinor analysis, developed by Franz Hallberg and colleagues in the 1960s, allows for the fitting of a cosine curve to data of a known period. Cosinor analysis is frequently used in the analysis of biological rhythm data. While software exists to perform these analyses, we are not aware of any published SAS procedures or macros which would facilitate them. METHODS: To meet this gap, we herein describe SAS macros which perform cosinor analyses that assume either normally or gamma distributed outcomes and fixed period. The macros can 1) produce datasets with cosinor parameters including acrophase, mesor, amplitude, nadir and test for rhythmicity 2) output datasets with fitted and observed values from the model, and 3) plot the resulting curve and underlying data. RESULTS: We demonstrate the use of these macros with data from our research on circadian rhythms of heart rate and sleep in critically ill patients. CONCLUSIONS: Cosinor analysis provides a parsimonious and intuitive set of estimates to summarize periodic data. We are hopeful that the publication of our macro will allow a wider spectrum of users to avail themselves of this technique.
BACKGROUND AND OBJECTIVE: Cosinor analysis, developed by Franz Hallberg and colleagues in the 1960s, allows for the fitting of a cosine curve to data of a known period. Cosinor analysis is frequently used in the analysis of biological rhythm data. While software exists to perform these analyses, we are not aware of any published SAS procedures or macros which would facilitate them. METHODS: To meet this gap, we herein describe SAS macros which perform cosinor analyses that assume either normally or gamma distributed outcomes and fixed period. The macros can 1) produce datasets with cosinor parameters including acrophase, mesor, amplitude, nadir and test for rhythmicity 2) output datasets with fitted and observed values from the model, and 3) plot the resulting curve and underlying data. RESULTS: We demonstrate the use of these macros with data from our research on circadian rhythms of heart rate and sleep in critically ill patients. CONCLUSIONS: Cosinor analysis provides a parsimonious and intuitive set of estimates to summarize periodic data. We are hopeful that the publication of our macro will allow a wider spectrum of users to avail themselves of this technique.
Authors: Gang Wu; Marc D Ruben; Robert E Schmidt; Lauren J Francey; David F Smith; Ron C Anafi; Jacob J Hughey; Ryan Tasseff; Joseph D Sherrill; John E Oblong; Kevin J Mills; John B Hogenesch Journal: Proc Natl Acad Sci U S A Date: 2018-10-30 Impact factor: 11.205
Authors: Michael E Hughes; Katherine C Abruzzi; Ravi Allada; Ron Anafi; Alaaddin Bulak Arpat; Gad Asher; Pierre Baldi; Charissa de Bekker; Deborah Bell-Pedersen; Justin Blau; Steve Brown; M Fernanda Ceriani; Zheng Chen; Joanna C Chiu; Juergen Cox; Alexander M Crowell; Jason P DeBruyne; Derk-Jan Dijk; Luciano DiTacchio; Francis J Doyle; Giles E Duffield; Jay C Dunlap; Kristin Eckel-Mahan; Karyn A Esser; Garret A FitzGerald; Daniel B Forger; Lauren J Francey; Ying-Hui Fu; Frédéric Gachon; David Gatfield; Paul de Goede; Susan S Golden; Carla Green; John Harer; Stacey Harmer; Jeff Haspel; Michael H Hastings; Hanspeter Herzel; Erik D Herzog; Christy Hoffmann; Christian Hong; Jacob J Hughey; Jennifer M Hurley; Horacio O de la Iglesia; Carl Johnson; Steve A Kay; Nobuya Koike; Karl Kornacker; Achim Kramer; Katja Lamia; Tanya Leise; Scott A Lewis; Jiajia Li; Xiaodong Li; Andrew C Liu; Jennifer J Loros; Tami A Martino; Jerome S Menet; Martha Merrow; Andrew J Millar; Todd Mockler; Felix Naef; Emi Nagoshi; Michael N Nitabach; Maria Olmedo; Dmitri A Nusinow; Louis J Ptáček; David Rand; Akhilesh B Reddy; Maria S Robles; Till Roenneberg; Michael Rosbash; Marc D Ruben; Samuel S C Rund; Aziz Sancar; Paolo Sassone-Corsi; Amita Sehgal; Scott Sherrill-Mix; Debra J Skene; Kai-Florian Storch; Joseph S Takahashi; Hiroki R Ueda; Han Wang; Charles Weitz; Pål O Westermark; Herman Wijnen; Ying Xu; Gang Wu; Seung-Hee Yoo; Michael Young; Eric Erquan Zhang; Tomasz Zielinski; John B Hogenesch Journal: J Biol Rhythms Date: 2017-11-03 Impact factor: 3.182
Authors: Cristina Godinho-Silva; Rita G Domingues; Miguel Rendas; Bruno Raposo; Hélder Ribeiro; Joaquim Alves da Silva; Ana Vieira; Rui M Costa; Nuno L Barbosa-Morais; Tânia Carvalho; Henrique Veiga-Fernandes Journal: Nature Date: 2019-09-18 Impact factor: 49.962
Authors: Margaret M Doyle; Terrence E Murphy; Brienne Miner; Margaret A Pisani; Elizabeth R Lusczek; Melissa P Knauert Journal: Sleep Med Date: 2022-02-08 Impact factor: 4.842