Methods of information in medicine, 2018

A Quadriparametric Model to Describe the Diversity of Waves Applied to Hormonal Data

Abdullah S, Bouchard T, Klich A, Leiva R, Pyper C, Genolini C, Subtil F, Iwaz J, Ecochard R

Author affiliations (6)
  • University of Sulaimani ROR
  • University of Calgary ROR
  • University of Ottawa ROR
  • University of Oxford ROR
  • Université Claude Bernard Lyon 1 ROR
  • Hospices Civils de Lyon ROR
DOI10.3414/me17-01-0102 PMID29719916
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Abstract

Background

Even in normally cycling women, hormone level shapes may widely vary between cycles and between women. Over decades, finding ways to characterize and compare cycle hormone waves was difficult and most solutions, in particular polynomials or splines, do not correspond to physiologically meaningful parameters.

Objective

We present an original concept to characterize most hormone waves with only two parameters.

Methods

The modelling attempt considered pregnanediol-3-alpha-glucuronide (PDG) and luteinising hormone (LH) levels in 266 cycles (with ultrasound-identified ovulation day) in 99 normally fertile women aged 18 to 45. The study searched for a convenient wave description process and carried out an extended search for the best fitting density distribution.

Results

The highly flexible beta-binomial distribution offered the best fit of most hormone waves and required only two readily available and understandable wave parameters: location and scale. In bell-shaped waves (e.g., PDG curves), early peaks may be fitted with a low location parameter and a low scale parameter; plateau shapes are obtained with higher scale parameters. I-shaped, J-shaped, and U-shaped waves (sometimes the shapes of LH curves) may be fitted with high scale parameter and, respectively, low, high, and medium location parameter. These location and scale parameters will be later correlated with feminine physiological events.

Conclusion

Our results demonstrate that, with unimodal waves, complex methods (e.g., functional mixed effects models using smoothing splines, second-order growth mixture models, or functional principal-component- based methods) may be avoided. The use, application, and, especially, result interpretation of four-parameter analyses might be advantageous within the context of feminine physiological events.

Topics

PMID 29719916 29719916 DOI 10.3414/me17-01-0102 10.3414/me17-01-0102 Abdullah et al. 2018, Abdullah 2018

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