Volume 25, Issue 9 pp. 1593-1618
Tutorial in Biostatistics

On the application of the von Mises distribution and angular regression methods to investigate the seasonality of disease onset

Fei Gao

Corresponding Author

Fei Gao

Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre, Singapore

Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre, 11 Hospital Drive, Singapore 169610, SingaporeSearch for more papers by this author
Kee-Seng Chia

Kee-Seng Chia

Centre for Molecular Epidemiology, National University of Singapore, Singapore

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Ingela Krantz

Ingela Krantz

Skaraborgsinstitutet, Stationgatan 12, Skövde SE 54130, Sweden

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Per Nordin

Per Nordin

Skaraborgsinstitutet, Stationgatan 12, Skövde SE 54130, Sweden

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David Machin

David Machin

Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre, Singapore

School of Health and Related Research, University of Sheffield, U.K.

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First published: 28 December 2005
Citations: 16

Abstract

This paper describes an approach to summarize the data arising from studies investigating the pattern of disease onset within a calendar year. Such data have been traditionally summarized into monthly counts summated over the complete years studied and patterns often examined by use of Pearson's χ2 tests with 11 degrees of freedom. This test and others commonly used in practice are reviewed. As an alternative, we suggest that by first representing the date of onset for an individual as a point on a unit circle that the von Mises distribution with a single peak may provide a useful description of such data. Further an extension to angular regression including covariates, analogous to that used routinely in other areas of clinical research, potentially allows a more systematic and detailed investigation of possible seasonal patterns in patient subgroups. The methodology is applied to examples from the date of onset of primary angle-closure glaucoma and date of diagnosis of acute lymphoblastic leukaemia and examines in both situations how the peak onset varies with covariates. Difficulties associated with convergence to the maximum likelihood estimates of the associated parameters are described. Finally, we emphasize the need for individualized (rather than grouped) patient data to be available for study, a clear specification of the particular ‘onset’ time studied, and suggest that further case studies are required to evaluate the approach. Copyright © 2005 John Wiley & Sons, Ltd.

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