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Assessing seasonality in clinical research

  • Ton J. Cleophas EMAIL logo and Aeilko H. Zwinderman
Published/Copyright: July 7, 2012
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 50 Issue 12

Abstract

Background: Seasonal patterns are assumed in many fields of medicine. However, biological processes are full of variations and the possibility of chance findings can often not be ruled out.

Methods: Using simulated data we assess whether autocorrelation is helpful to minimize chance findings and test to support the presence of seasonality.

Results: Autocorrelation required to cut time curves into pieces. These pieces were compared with one another using linear regression analysis. Four examples with imperfect data are given. In spite of substantial differences in the data between the first and second year of observation, and in spite of otherwise inconsistent patterns, significant positive autocorrelations were constantly demonstrated with correlation coefficients around 0.40 (SE 0.14).

Conclusions: Our data suggest that autocorrelation is helpful to support the presence of seasonality of disease, and that it does so even with imperfect data.

Keywords: autocorrelation; cross-correlation; partial autocorrelation; seasonality; time series plots
Corresponding author: Ton J. Cleophas, Department of Medicine, Albert Schweitzer Hospital, Box 400, 3300 AK Dordrecht, The Netherlands

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Received: 2012-05-10
Accepted: 2012-06-08
Published Online: 2012-07-07
Published in Print: 2012-12-01

©2012 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/cclm-2012-0295
Keywords for this article
autocorrelation; cross-correlation; partial autocorrelation; seasonality; time series plots

Articles in the same Issue

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  2. Masthead
  3. Editorials
  4. Phlebotomy, stat testing and laboratory organization: an intriguing relationship
  5. Human epididymis protein 4: the start of a post-ROMAn era?
  6. Hyperhomocysteinemia in health and disease: where we are now, and where do we go from here?
  7. Reviews
  8. The usefulness of cystatin C and related formulae in pediatrics
  9. The emerging role of biomarkers and bio-impedance in evaluating hydration status in patients with acute heart failure
  10. Biomarkers in primary open angle glaucoma
  11. Mini Review
  12. Identification of circulating microRNAs as biomarkers in cancers: what have we got?
  13. Opinion Paper
  14. HE4 in gynecological cancers: report of a European investigators and experts meeting
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  18. Reducing the number of clinical stat phlebotomy orders: feasible or not?
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  20. UrineCART, a machine learning method for establishment of review rules based on UF-1000i flow cytometry and dipstick or reflectance photometer
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  41. Acknowledgment
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