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Restricted cubic splines for modelling periodic data.

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  • 1Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper/Capodistria, Slovenia.

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|October 28, 2020
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Summary
This summary is machine-generated.

This study introduces periodic restricted cubic splines (RCS) for modeling time-varying outcomes. Periodic RCS effectively capture seasonal variations and hormonal fluctuations, outperforming existing methods for periodic data analysis.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Computational Biology

Background:

  • Non-linear relationships in regression are often modeled using restricted cubic splines (RCS).
  • Periodic changes in outcomes, such as seasonal variations or hormonal fluctuations, require specialized modeling techniques.
  • Existing methods for periodic data may have limitations in accuracy or parameter estimation.

Purpose of the Study:

  • To extend restricted cubic splines (RCS) to effectively model periodic data by incorporating numerical constraints.
  • To evaluate the performance of periodic RCS against other methods for analyzing time-varying outcomes with periodicity.
  • To provide a practical and accessible tool for modeling periodic phenomena in various scientific fields.

Main Methods:

  • Development of an extension of restricted cubic splines (RCS) that incorporates numerical constraints to handle periodicity.
  • Application of periodic RCS to real and simulated data with binary outcomes.
  • Comparison of periodic RCS with other established methods for modeling periodic data, including cosinor models.

Main Results:

  • Periodic RCS demonstrate superior performance compared to other methods for periodic data, particularly in reducing estimate variability at the extremes of the period.
  • Periodic RCS require estimation of fewer parameters than traditional cubic spline methods.
  • Cosinor models perform comparably to the best cubic spline models but require appropriate harmonic selection for optimal performance.

Conclusions:

  • Periodic restricted cubic splines (RCS) offer a valuable extension for modeling periodic data when the outcome is assumed to be equal at the beginning and end of the period.
  • The peRiodiCS R package provides a free implementation for applying periodic RCS.
  • Periodic RCS are effective for modeling phenomena like seasonal virus occurrence and hormonal cycles.