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Modeling basal body temperature data using horseshoe process regression.

Elizabeth C Chase1, Jeremy M G Taylor2, Philip S Boonstra2

  • 1Statistics Group, RAND Corporation, Arlington, VA, USA.

Statistics in Medicine
|December 14, 2023
PubMed
Summary
This summary is machine-generated.

Horseshoe process regression (HPR) models biomedical data with abrupt changes, like basal body temperature shifts during the menstrual cycle. This new Bayesian approach accurately captures sharp changes without oversmoothing or overfitting.

Keywords:
horseshoe priorlocal shrinkagemenstrual cyclenonparametricsstep functions

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Area of Science:

  • Biostatistics
  • Bayesian modeling
  • Time series analysis

Background:

  • Biomedical data frequently display abrupt changes, posing modeling challenges.
  • Existing methods often struggle with sharp transitions, leading to oversmoothing or overfitting.
  • Accurate modeling of such data is crucial for understanding biological processes.

Purpose of the Study:

  • To introduce a novel nonparametric Bayesian prior, horseshoe process regression (HPR), for modeling biomedical data with abrupt changes.
  • To provide a flexible and robust statistical framework for analyzing complex biological signals.
  • To enhance the analysis of time-series biomedical data, such as basal body temperature.

Main Methods:

  • Developed horseshoe process regression (HPR) using a horseshoe-distributed increment process.
  • Implemented HPR as a nonparametric Bayesian prior within a regression framework using Stan.
  • Introduced extensions including Bayesian imputation, covariate inclusion, and monotonicity constraints.

Main Results:

  • HPR effectively models functions exhibiting sharp changes, outperforming traditional methods.
  • The method demonstrated good performance in fitting complex, nonlinear associations.
  • Applied successfully to model basal body temperature variations throughout the menstrual cycle.

Conclusions:

  • Horseshoe process regression (HPR) offers a powerful new tool for analyzing biomedical data with abrupt changes.
  • The developed framework provides flexibility and accuracy for biological signal modeling.
  • HPR advances the statistical toolkit for understanding dynamic physiological processes.