Limits...
The coefficient of cyclic variation: a novel statistic to measure the magnitude of cyclic variation.

Fulford AJ - Emerg Themes Epidemiol (2014)

Bottom Line: PERIODIC OR CYCLIC DATA OF KNOWN PERIODICITY ARE FREQUENTLY ENCOUNTERED IN EPIDEMIOLOGICAL AND BIOMEDICAL RESEARCH: for instance, seasonality provides a useful experiment of nature while diurnal rhythms play an important role in endocrine secretion.There is, however, little consensus on how to analysis these data and less still on how to measure association or effect size for the often complex patterns seen.The coefficient of cyclic variation or similar statistics derived from the variance of a Fourier series could provide a universal means of summarising the magnitude of periodic variation.

View Article: PubMed Central - HTML - PubMed

Affiliation: MRC Keneba, MRC Unit, P.O. Box 273, Banjul, The Gambia ; MRC International Nutrition Group, Department Public Health, London School of Hygiene & Tropical Medicine, London, UK.

ABSTRACT

Background: PERIODIC OR CYCLIC DATA OF KNOWN PERIODICITY ARE FREQUENTLY ENCOUNTERED IN EPIDEMIOLOGICAL AND BIOMEDICAL RESEARCH: for instance, seasonality provides a useful experiment of nature while diurnal rhythms play an important role in endocrine secretion. There is, however, little consensus on how to analysis these data and less still on how to measure association or effect size for the often complex patterns seen.

Results: A simple statistic, readily derived from Fourier regression models, provides a readily-understood measure cyclic variation in a wide variety of situations.

Conclusion: The coefficient of cyclic variation or similar statistics derived from the variance of a Fourier series could provide a universal means of summarising the magnitude of periodic variation.

No MeSH data available.


Related in: MedlinePlus

Seasonal patterns of vitamin B6 biomarker levels in plasma of Gambian women. Pyridoxal = light grey; pyridoxic acid = dark grey; pyridoxal phosphate = black. The data were derived from 315 samples on 52 women observed across the year. Curves show the plasma concentration (mg/dl) estimated using generalised least squares random effects fitting the first four Fourier terms.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4196209&req=5

Figure 2: Seasonal patterns of vitamin B6 biomarker levels in plasma of Gambian women. Pyridoxal = light grey; pyridoxic acid = dark grey; pyridoxal phosphate = black. The data were derived from 315 samples on 52 women observed across the year. Curves show the plasma concentration (mg/dl) estimated using generalised least squares random effects fitting the first four Fourier terms.

Mentions: 2. This example employing the ccv is shown in Figure 2. Here the seasonal patterns of plasma pyridoxal, pyridoxal phosphate and pyridoxic acid derived from 315 observations of 52 Gambian women (data courtesy of Paula Dominguez-Salas). The logarithms of the assay values were fitted by random effects GLS regression using the first four Fourier terms and controlling for age; the fitted values were anti-logged to yield the plotted curves. All three biomarkers appear to follow the same seasonal influences peaking in May but are they affected equally? Although a thorough analysis would make allowance for the fact that these biomarkers were measured in the same women, from the ccvs and their 95% confidence intervals tabulated in Table 1 it is clear that the seasonal patterns are of very similar magnitude. Note also the consistency between the two methods used to estimate the confidence intervals.


The coefficient of cyclic variation: a novel statistic to measure the magnitude of cyclic variation.

Fulford AJ - Emerg Themes Epidemiol (2014)

Seasonal patterns of vitamin B6 biomarker levels in plasma of Gambian women. Pyridoxal = light grey; pyridoxic acid = dark grey; pyridoxal phosphate = black. The data were derived from 315 samples on 52 women observed across the year. Curves show the plasma concentration (mg/dl) estimated using generalised least squares random effects fitting the first four Fourier terms.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4196209&req=5

Figure 2: Seasonal patterns of vitamin B6 biomarker levels in plasma of Gambian women. Pyridoxal = light grey; pyridoxic acid = dark grey; pyridoxal phosphate = black. The data were derived from 315 samples on 52 women observed across the year. Curves show the plasma concentration (mg/dl) estimated using generalised least squares random effects fitting the first four Fourier terms.
Mentions: 2. This example employing the ccv is shown in Figure 2. Here the seasonal patterns of plasma pyridoxal, pyridoxal phosphate and pyridoxic acid derived from 315 observations of 52 Gambian women (data courtesy of Paula Dominguez-Salas). The logarithms of the assay values were fitted by random effects GLS regression using the first four Fourier terms and controlling for age; the fitted values were anti-logged to yield the plotted curves. All three biomarkers appear to follow the same seasonal influences peaking in May but are they affected equally? Although a thorough analysis would make allowance for the fact that these biomarkers were measured in the same women, from the ccvs and their 95% confidence intervals tabulated in Table 1 it is clear that the seasonal patterns are of very similar magnitude. Note also the consistency between the two methods used to estimate the confidence intervals.

Bottom Line: PERIODIC OR CYCLIC DATA OF KNOWN PERIODICITY ARE FREQUENTLY ENCOUNTERED IN EPIDEMIOLOGICAL AND BIOMEDICAL RESEARCH: for instance, seasonality provides a useful experiment of nature while diurnal rhythms play an important role in endocrine secretion.There is, however, little consensus on how to analysis these data and less still on how to measure association or effect size for the often complex patterns seen.The coefficient of cyclic variation or similar statistics derived from the variance of a Fourier series could provide a universal means of summarising the magnitude of periodic variation.

View Article: PubMed Central - HTML - PubMed

Affiliation: MRC Keneba, MRC Unit, P.O. Box 273, Banjul, The Gambia ; MRC International Nutrition Group, Department Public Health, London School of Hygiene & Tropical Medicine, London, UK.

ABSTRACT

Background: PERIODIC OR CYCLIC DATA OF KNOWN PERIODICITY ARE FREQUENTLY ENCOUNTERED IN EPIDEMIOLOGICAL AND BIOMEDICAL RESEARCH: for instance, seasonality provides a useful experiment of nature while diurnal rhythms play an important role in endocrine secretion. There is, however, little consensus on how to analysis these data and less still on how to measure association or effect size for the often complex patterns seen.

Results: A simple statistic, readily derived from Fourier regression models, provides a readily-understood measure cyclic variation in a wide variety of situations.

Conclusion: The coefficient of cyclic variation or similar statistics derived from the variance of a Fourier series could provide a universal means of summarising the magnitude of periodic variation.

No MeSH data available.


Related in: MedlinePlus