Clustering in sparse data and an analysis of rhabdomyosarcoma incidence

Clustering in sparse data and an analysis of rhabdomyosarcoma incidence Time series of epidemiologic events often contain periods of atypically low or high frequency. Correspondingly, for quite rare diseases there occur instances of long vacuous durations interrupted noticeably by periods of some disease activity. A recent community‐based observation of the incidence of rhabdomyosarcoma (RMS), and an investigation of it, yielded sparse data of this general description. We introduce a combinatorial test for patchy time series and apply it to the RMS data. We comment on the prevalent practice of post hoc data analysis of alleged clusters, and on scale effects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

Clustering in sparse data and an analysis of rhabdomyosarcoma incidence

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Abstract

Time series of epidemiologic events often contain periods of atypically low or high frequency. Correspondingly, for quite rare diseases there occur instances of long vacuous durations interrupted noticeably by periods of some disease activity. A recent community‐based observation of the incidence of rhabdomyosarcoma (RMS), and an investigation of it, yielded sparse data of this general description. We introduce a combinatorial test for patchy time series and apply it to the RMS data. We comment on the prevalent practice of post hoc data analysis of alleged clusters, and on scale effects.

Journal

Statistics in MedicineWiley

Published: Jan 1, 1992

References

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