Power Comparisons in 2x2 Contingency Tables: Odds Ratio versus Pearson Correlation versus Canonical Correlation

TL;DR

This paper compares the statistical power of odds ratio, Pearson correlation, and canonical correlation tests for assessing association in 2x2 binary tables, revealing no single test is universally superior.

Contribution

It introduces a comprehensive comparison of these tests' power in binary data, highlighting the strengths and limitations of each method.

Findings

No test consistently outperforms the others across all scenarios.

Pearson correlation extends to higher-order tables unlike odds ratio.

The tests show comparable power depending on the data distribution.

Abstract

It is an important inferential problem to test no association between two binary variables based on data. Tests based on the sample odds ratio are commonly used. We bring in a competing test based on the Pearson correlation coefficient. In particular, the Odds ratio does not extend to higher order contingency tables, whereas Pearson correlation does. It is important to understand how Pearson correlation stacks against the odds ratio in 2x2 tables. Another measure of association is the canonical correlation. In this paper, we examine how competitive Pearson correlation is vis-\`a-vis odds ratio in terms of power in the binary context, contrasting further with both the Wald Z and Rao Score tests. We generated an extensive collection of joint distributions of the binary variables and estimated the power of the tests under each joint alternative distribution based on random samples. The consensus is that none of the tests dominates the other.