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This study introduces a new Bayesian method for analyzing multilevel compositional data, common in health research. The method, supported by the multilevelcoda R package, shows excellent performance and accuracy in simulations.

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Area of Science:

  • Statistics
  • Data Science
  • Biostatistics

Background:

  • Multilevel compositional data are nonnegative, sum to a constant, and are clustered within groups.
  • These data are prevalent in longitudinal studies, ecological momentary assessments, and wearable device data, such as sleep patterns and dietary intake.

Purpose of the Study:

  • To present an innovative Bayesian inference method for analyzing multilevel compositional data.
  • To introduce the R package multilevelcoda to facilitate the application of this novel analytical approach.

Main Methods:

  • Developed a Bayesian multilevel compositional data analysis framework.
  • Validated the method through an extensive parameter recovery simulation study.
  • Utilized the R package multilevelcoda for implementation and illustration with a real data example.

Main Results:

  • The simulation study demonstrated robust performance across all investigated conditions.
  • Fitted models exhibited minimal convergence issues (convergence rate > 99%).
  • Achieved excellent parameter estimates and inference quality, with low bias (average 0.00) and high coverage (average 0.95).

Conclusions:

  • The proposed Bayesian method offers a reliable and accurate approach for analyzing multilevel compositional data.
  • The multilevelcoda R package simplifies the application of this method, promoting its wider use in scientific research.
  • This method can yield novel and robust insights from complex clustered, nonnegative, constant-sum data.