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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Ranking subjects based on paired compositional data with application to age-related hearing loss subtyping.

Jin Hyun Nam1, Aastha Khatiwada1, Lois J Matthews2

  • 1Department of Public Health Sciences, Medical University of South Carolina, USA.

Communications for Statistical Applications and Methods
|June 23, 2020
PubMed
Summary

This study introduces a new method for analyzing paired compositional data, specifically for ranking subjects with age-related hearing loss (presbyacusis). The approach effectively classifies and ranks individuals within presbyacusis phenotypes.

Keywords:
compositionelastic netpenalized logistic regressionpresbyacusis

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Area of Science:

  • Biostatistics
  • Data Science
  • Otolaryngology

Background:

  • Established methods for single compositional data analysis exist, but paired compositional data analysis strategies require further investigation.
  • Age-related hearing loss (presbyacusis) presents a challenge due to the need to rank subjects within audiometric phenotypes using paired compositional data.

Purpose of the Study:

  • To develop and validate an effective analysis strategy for paired compositional data.
  • To address the challenge of ranking subjects within presbyacusis phenotypes using their paired compositional data.

Main Methods:

  • Formulated the problem as a classification task, integrating a penalized multinomial logistic regression model with compositional data analysis.
  • Employed Elastic Net for the penalty function and utilized average, absolute difference, and perturbation operators for compositional data.
  • Applied the proposed approach to a study of 532 subjects with presbyacusis, analyzing probabilities of ear involvement in four subtypes.

Main Results:

  • The proposed approach demonstrated effectiveness in classifying and ranking subjects based on paired compositional data.
  • Analysis of presbyacusis data indicated successful subject ranking within phenotypes.

Conclusions:

  • The developed penalized multinomial logistic regression model integrated with compositional data analysis provides an effective solution for paired compositional data.
  • This method offers a robust approach for ranking subjects in studies involving complex data structures like presbyacusis phenotypes.