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The Statistical Analysis of Circadian Phase and Amplitude in Constant-Routine Core-Temperature Data

Brown, Emery N., Czeisler, Charles A.·1992·Journal of Biological Rhythms

Abstract

Accurate estimation of the phases and amplitude of the endogenous circadian pacemaker from constant-routine core-temperature series is crucial for making inferences about the properties of the human biological clock from data collected under this protocol. This paper presents a set of statistical methods based on a harmonic-regression-plus-correlated-noise model for estimating the phases and the amplitude of the endogenous circadian pacemaker from constant-routine core-temperature data. The methods include a Bayesian Monte Carlo procedure for computing the uncertainty in these circadian functions. We illustrate the techniques with a detailed study of a single subject's core-temperature series and describe their relationship to other statistical methods for circadian data analysis. In our laboratory, these methods have been successfully used to analyze more than 300 constant routines and provide a highly reliable means of extracting phase and amplitude information from core-temperature data.

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Metadata

Authors
Brown, E. N., & Czeisler, C. A.
Venue
Journal of Biological Rhythms
Published
1992-10
Volume
7
Issue
3
Pages
177-202
ISSN
  • 0748-7304 (ISSN-L)
  • 1552-4531
OpenAlex
W2024601773
Cited by
224
References
2
FWCI
5.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Statistical Analysis of Circadian Phase and Amplitude in Constant-Routine Core-Temperature Data. https://app.cochat.ai/papers/10.1177/074873049200700301
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Brown, E. N., & Czeisler, C. A. (1992). The Statistical Analysis of Circadian Phase and Amplitude in Constant-Routine Core-Temperature Data. Journal of Biological Rhythms. https://doi.org/10.1177/074873049200700301

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.