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[Non-parametric Bootstrap estimation on the intraclass correlation coefficient generated from quantitative

Rong Liang1, Shu-dong Zhou1, Li-xia Li1

  • 1Department of Epidemiology and Biostatistics, Guangdong Pharmaceutical Uniersity, Guangzhou 510310, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
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Summary
This summary is machine-generated.

This study introduces bootstrapping methods for hierarchical data to estimate confidence intervals for the intraclass correlation coefficient (ICC). Cluster bootstrapping is recommended for accurate ICC confidence intervals, especially in complex data structures.

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Context:

  • Hierarchical data structures are common in various scientific fields, including medicine, psychology, and ecology.
  • Accurate estimation of the intraclass correlation coefficient (ICC) is crucial for understanding data variability and designing studies.
  • Traditional methods may struggle with the complexities of nested or clustered data, necessitating robust statistical approaches.

Purpose:

  • To develop and evaluate bootstrapping methods for estimating confidence intervals (CIs) of the intraclass correlation coefficient (ICC) in hierarchical data.
  • To compare the performance of different bootstrapping strategies (e.g., cluster vs. random bootstrapping) under various sampling designs.
  • To provide practical guidance on selecting appropriate resampling techniques for hierarchical data analysis.

Summary:

  • Mixed-effects models were used to estimate ICC from repeated measurements and two-stage sampling data.
  • Bootstrap methods were applied to estimate CIs for ICCs, with cluster bootstrapping demonstrating superior performance in capturing true ICC values.
  • Ignoring data hierarchy in random bootstrapping led to invalid CIs, highlighting the importance of accounting for data structure during resampling.

Impact:

  • The findings underscore the necessity of considering data structure during resampling of hierarchical data.
  • Cluster bootstrapping emerges as a reliable method for obtaining accurate ICC confidence intervals in complex hierarchical datasets.
  • This research offers improved statistical tools for researchers working with nested or clustered data, enhancing the validity of their findings.