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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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Generalized Confidence Intervals for Intra- and Inter-subject Coefficients of Variation in Linear Mixed-effects

Johannes Forkman1

  • 1.

The International Journal of Biostatistics
|July 5, 2017
PubMed
Summary

This study introduces a new method for calculating confidence intervals for intra- and inter-subject coefficients of variation. These methods are crucial for understanding variability in linear mixed-effects models and were validated in bioanalytical and agricultural settings.

Keywords:
bioanalytical method validationgeneralized pivotal quantitylinear mixed modelsemiparametric mixed-effects modelsplit-plot experiment

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Area of Science:

  • Statistics
  • Biostatistics
  • Agricultural Science

Background:

  • Linear mixed-effects models are widely used for analyzing data with hierarchical structures.
  • These models involve variance components, specifically inter-subject (random-effects) and intra-subject (residual error) variances.
  • Reporting variance components as coefficients of variation is common practice.

Purpose of the Study:

  • To propose novel methods for computing confidence intervals for intra- and inter-subject coefficients of variation.
  • To address the need for reliable measures of variability in complex data structures.
  • To provide practical tools for assessing precision and variation in scientific applications.

Main Methods:

  • Utilizing generalized pivotal quantities to construct confidence intervals.
  • Applying the proposed methods to real-world examples in bioanalytical method validation and agricultural split-plot experiments.
  • Conducting simulation studies to evaluate the coverage properties of the generalized confidence intervals.

Main Results:

  • The proposed methods provide reliable confidence intervals for coefficients of variation.
  • The generalized confidence intervals demonstrate coverage close to the nominal value in simulations.
  • The methods are effectively illustrated through practical examples, showcasing their utility.

Conclusions:

  • Generalized pivotal quantities offer a robust approach for estimating confidence intervals of coefficients of variation.
  • The developed methods enhance the assessment of intra- and inter-subject variability in linear mixed-effects models.
  • This work provides valuable statistical tools for researchers in various scientific disciplines.