Equivalence of Generalized Bivariate Bernoulli Dependency Test & Re-parameterize Logistic Regression

Music City Center 

Background: Binary endpoints at two timepoints (e.g., pre- vs. post-treatment) are common in healthcare research. The Generalized Bivariate Bernoulli Model (GBBM) is a specialized GLM for bivariate binary data but lacks software for direct analysis. Additionally, the original comparison of the GBBM dependency test to regressive logistic regression is flawed.
Methods: We propose a re-parameterized logistic regression model, proving its equivalence to the GBBM dependency test theoretically and empirically. Simulations compare the power of the GBBM test with a) the regressive logistic model, b) our re-parameterized logistic model, and c) the Pearson Chi-square test. We also analyze infant mortality data from BDHS.
Results: The GBBM test's power differs from the regressive logistic model but matches our re-parameterized logistic model across effect and sample sizes.
Conclusion: This study refines dependency analysis in bivariate binary data, enhancing accessibility for researchers.

Longitudinal binary endpoints

generalized linear models

repeated measures 

ASA LGBTQ+ Advocacy Committee