Most variables of clinical interest show predictable changes with different frequencies, mainly, but not exclusively, along the rest–activity cycle (circadian variation). Methods of linear least-squares estimation have been designed for the detection of periodic components in sparse and noisy time series (as they are usually present in clinical situations). They include the single and population-mean cosinor methods. In cases where more than one period is statistically significant over the span of time investigated, or when the waveform is non-sinusoidal, the use of multiple components analysis to fit a model consisting of several cosine functions (harmonics or not from a given fundamental period) is recommended. We describe these methods, from the characterization of the underlying models to the process of parameter estimation. As an application example, we describe the modelling of the circadian variation of blood pressure (BP). In most individuals, BP presents a morning increase, a small postprandial valley and a deeper descent during nocturnal rest. This pattern can be easily modelled by means of a model with periods of 24 and 12 hours. Individuals that differ from this model might be considered to present increased cardiovascular risk.
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