Study design and data analysis are two important aspects relevant to chronopharmacometrics. Blunders can be avoided by recognizing that most physiological variables are circadian periodic. Both ill health and treatment can affect the amplitude, phase, and/or period of circadian (and other) rhythms, in addition to their mean. The involvement of clock genes in molecular pathways related to important physiological systems underlies the bidirectional relationship often seen between circadian rhythm disruption and disease risk. Circadian rhythm characteristics of marker rhythms interpreted in the light of chronobiologic reference values represent important diagnostic tools. A set of cosinor-related programs is presented. They include the least squares fit of multiple-frequency cosine functions to model the time structure of individual records; a cosinor-based spectral analysis to detect periodic signals; the population-mean cosinor to generalize inferences; the chronobiologic serial section to follow the time course of changing rhythm parameters over time; and parameter tests to assess differences among populations. Relative merits of other available cosinor and non-parametric algorithms are reviewed. Parameter tests to compare individual records and a self-starting cumulative sum (CUSUM) make personalized chronotherapy possible, where the treatment of each patient relies on an N-of-1 design. Methods are illustrated in a few examples relevant to endocrinology, cancer and cardiology. New sensing technology yielding large personal data sets is likely to change the healthcare system. Chronobiologic concepts and methods should become an integral part of these evolving systems.
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© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Chronobiologic study design
- Marker rhythm
- N-of-1 design