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Sinusoidal cox regression-a rare cancer example.

Jimmy Thomas Efird1

  • 1Epidemiologist/Chief Statistician and Director of Shared Resources, Center for Health Disparities Research, Brody School of Medicine, East Carolina University, 1800 W. 5th Street (Medical Pavilon), Greenville, NC 27834 USA.

Cancer Informatics
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PubMed
Summary
This summary is machine-generated.

This study introduces a robust statistical method to analyze seasonal birth date patterns and patient survival. The trigonometric Cox regression model offers a powerful alternative for detecting environmental exposures linked to birth timing and health outcomes.

Keywords:
seasonality of birthsinusoidal Cox regression

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Environmental Health

Background:

  • Seasonal variations in environmental exposures may influence perinatal health and long-term survival.
  • Traditional methods of analyzing birth date seasonality often involve arbitrary categorizations and multiple comparisons, potentially reducing statistical power.

Purpose of the Study:

  • To present a statistically robust method for analyzing the cyclic nature of birth dates in relation to patient survival.
  • To offer an alternative to traditional, less powerful methods of assessing seasonal birth date associations.

Main Methods:

  • Utilized a trigonometric Cox regression model to analyze survival time and date of birth.
  • Employed a derivative-free approach for statistical analysis.
  • Visualized results using a sinusoidal plot indicating peak risk dates and a single P-value for overall significance.

Main Results:

  • The trigonometric Cox regression model provides a statistically robust analysis of seasonal birth date effects on survival.
  • The method allows for the identification of specific peak dates of relative risk.
  • A single P-value determines the overall statistical significance of the seasonal association.

Conclusions:

  • The trigonometric Cox regression model is a powerful and user-friendly tool for investigating seasonal influences on health outcomes related to birth timing.
  • This approach enhances the ability to detect statistically significant associations and avoids subjective seasonal demarcations.