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Partitioned method of valid moment marginal model with Bayes interval estimates for correlated binary data with time-dependent covariates

Author

Listed:
  • Elsa Vazquez

    (Arizona State University)

  • Jeffrey R. Wilson

    (Arizona State University)

Abstract

The fit of marginal models to longitudinal data should include modelling all extra variation among responses and covariates. This paper proposes a Partitioned Method of Valid Moments marginal regression model for binary outcomes with Bayes method while using lagged coefficients. Time-dependent covariates are factored in through composite likelihoods. A simulation study highlights the properties of the model coefficients. Modeling cognitive impairment diagnosis in NACC Alzheimer clinical data are demonstrated. Sensitivity analyses are conducted to evaluate the impact of the prior distribution on the posterior inferences.

Suggested Citation

  • Elsa Vazquez & Jeffrey R. Wilson, 2021. "Partitioned method of valid moment marginal model with Bayes interval estimates for correlated binary data with time-dependent covariates," Computational Statistics, Springer, vol. 36(4), pages 2701-2718, December.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:4:d:10.1007_s00180-021-01105-3
    DOI: 10.1007/s00180-021-01105-3
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    References listed on IDEAS

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