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Related Experiment Videos

Bonferroni-based correction factor for multiple, correlated endpoints.

Qian Shi¹, Emily S Pavey, Rickey E Carter

¹Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. shi.qian2@mayo.edu

Pharmaceutical Statistics

|May 17, 2012

Summary

This study introduces an improved Bonferroni adjustment for multiple testing, enhancing statistical power with correlated data. The novel method maintains simplicity while offering better performance in clinical trials.

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Area of Science:

Biostatistics
Statistical Methods

Background:

Multiple testing impacts Type I and Type II error rates in statistical and biomedical research.
The traditional Bonferroni adjustment is widely used but performs poorly with correlated test statistics, often being too conservative.

Purpose of the Study:

To propose a novel adaptation of the Bonferroni adjustment that accounts for correlated data.
To overcome the limitations of the standard Bonferroni method while retaining its ease of use and intuitive explanation.

Main Methods:

Developed a new correction factor based on intraclass correlation.
Applied this factor to the standard Bonferroni method to adjust for correlated test statistics.
Evaluated the method through a detailed simulation study.

Main Results:

The proposed adaptation demonstrates statistical soundness and appropriate performance across various settings.
The method effectively addresses the conservatism of the traditional Bonferroni adjustment with correlated data.

Conclusions:

The novel Bonferroni adaptation offers a statistically robust and practical solution for multiple testing with correlated data.
This approach is suitable for applications like early-phase clinical trials with multiple correlated outcome measures, such as those assessing marijuana craving.