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Association models for clustered data with binary and continuous responses.

Lanjia Lin1, Dipankar Bandyopadhyay, Stuart R Lipsitz

  • 1Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA. lanjia@stat.fsu.edu

Biometrics
|May 13, 2009
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Summary
This summary is machine-generated.

This study introduces a novel bivariate random effects model for analyzing clustered data with both binary and continuous outcomes. The proposed method effectively captures complex associations and offers robust parameter estimation, as shown in a developmental toxicity study.

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Developmental Toxicology

Background:

  • Analysis of clustered data with multiple response types (binary and continuous) presents statistical challenges.
  • Existing models may not adequately capture the complex associations present in such data.
  • Understanding these associations is crucial in fields like developmental toxicology.

Purpose of the Study:

  • To propose a new bivariate random effects model for clustered data with mixed binary and continuous outcomes.
  • To develop a model that accounts for various levels of association: within binary, within continuous, between binary and continuous across subjects, and within subjects.
  • To ensure interpretable marginal models for both binary and continuous responses.

Main Methods:

  • Development of a bivariate random effects model incorporating multiple association structures.
  • Marginal models designed to preserve logistic form for binary probabilities and linear form for continuous expectations.
  • Implementation using maximum likelihood estimation via standard statistical software (e.g., PROC NLMIXED in SAS).

Main Results:

  • Simulation studies confirmed the method's robustness against misspecification of both regression and random effects models.
  • The model successfully illustrates complex associations in clustered bivariate response data.
  • Application to a developmental toxicity study of ethylene glycol in mice demonstrated practical utility.

Conclusions:

  • The proposed bivariate random effects model provides a flexible and robust framework for analyzing clustered data with mixed outcomes.
  • The model's ability to capture intricate associations and maintain interpretable marginal forms enhances its applicability.
  • This methodology offers a valuable tool for statistical analysis in toxicological and other research areas involving complex clustered data.