Applications of cosinor rhythmometry in pharmacology.

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.

N-of-1 Design and Its Applications to Personalized Treatment Studies.

N-of-1 trial is a type of clinical trial which has been applied in chronic recurrent conditions that require long-term non-curative treatment. In this type of trials, each patient will be randomly assigned to one of the treatment sequences and repeatedly crossed over two or more treatments of interests. Through this cross-comparing method (cross-over phase), investigator can identify an optimal treatment (medicine or therapy) for the patient and treat the patient with the optimal treatment in an extension phase. This design could efficiently reduce the placebo effect, which is often seen in clinical trials, and maximize the true treatment effect. This type of design has been used in some traditional Chinese medicine (TCM) clinical trials lately. However, it brings some challenges for collecting and analyzing the data. Research on statistical methodology of this type of design is rarely found in the literature. The goal of this research is to discuss the application of the N-of-1 design to personalized treatment studies. We will demonstrate a real study conducted in TCM and present some theoretical and simulation results.