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Association models for a multivariate binary response.

A Ekholm1, J W McDonald, P W Smith

  • 1Rolf Nevanlinna Institute, University of Helsinki, Finland. anders.ekholm@helsinki.fi

Biometrics
|September 14, 2000
PubMed
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This study introduces novel multivariate binary response models using dependence ratios for better inference. These models offer flexible ways to analyze complex binary data, outperforming traditional methods.

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.

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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.