Bootstrap Estimate of Bias for Intraclass Correlation

Area of Science:

• Statistics
• Biostatistics
• Psychometrics

Background:

• Intraclass correlation estimates are prone to bias.
• Analytical methods for bias assessment often rely on normality assumptions.
• Limited analytical approaches exist to quantify bias in intraclass correlation estimates.

Purpose of the Study:

• To introduce and demonstrate cluster bootstrapping for estimating bias in intraclass correlation.
• To provide a method for bias assessment that does not rely on model assumptions.
• To illustrate the practical application of bias estimation in a real-world dataset.

Main Methods:

• Utilized cluster bootstrapping technique.
• Applied the method to a well-known dataset for intraclass correlation analysis.
• Calculated bias in intraclass correlation estimation.

Main Results:

• Demonstrated the bias in intraclass correlation estimates using a practical example.
• Showcased the effectiveness of cluster bootstrapping in estimating bias.
• Highlighted the implications of bias for different study designs.

Conclusions:

• Cluster bootstrapping is a valuable, assumption-free method for estimating bias in intraclass correlation.
• This approach enhances the reliability of statistical findings in studies utilizing intraclass correlation.
• The findings have broad implications for the design and interpretation of clustered data studies.