Association models for a multivariate binary response

Area of Science:

• Statistics
• Biostatistics
• Statistical Modeling

Background:

• Multivariate binary data analysis is crucial in many fields.
• Existing models often struggle with complex dependence structures.
• A new parameterization is needed for robust inference.

Purpose of the Study:

• To develop and present a new class of models for multivariate binary responses.
• To parameterize models using marginal probabilities and dependence ratios.
• To facilitate likelihood-based inference for regression and association parameters.

Main Methods:

• Proposed five association models based on dependence ratios.
• Models include latent factors and Markov chain structures.
• Illustrated methods with reanalyzed datasets, including time-series data.

Main Results:

• Demonstrated the utility of dependence ratio parameterization for inference.
• Contrasted likelihood-based approaches with generalized estimating equations.
• Showcased the flexibility of proposed association models.

Conclusions:

• Dependence ratio models provide a powerful alternative for multivariate binary data.
• The proposed association models offer interpretable mechanisms for dependence.
• This framework enhances the analysis of complex binary outcomes.