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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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    1. Richard E. Chandler & Steven Bate, 2007. "Inference for clustered data using the independence loglikelihood," Biometrika, Biometrika Trust, vol. 94(1), pages 167-183.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    4. Michael L. Stein & Zhiyi Chi & Leah J. Welty, 2004. "Approximating likelihoods for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 275-296, May.
    5. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    6. C. A. Glasbey, 2001. "Non‐linear autoregressive time series with multivariate Gaussian mixtures as marginal distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 143-154.
    7. Bradley Efron, 2015. "Frequentist accuracy of Bayesian estimates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 617-646, June.
    8. Viola Obermeier & Fabian Scheipl & Christian Heumann & Joachim Wassermann & Helmut Küchenhoff, 2015. "Flexible distributed lags for modelling earthquake data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(2), pages 395-412, February.
    9. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
    10. Patrick J. Heagerty & Bryan A. Comstock, 2013. "Exploration of Lagged Associations using Longitudinal Data," Biometrics, The International Biometric Society, vol. 69(1), pages 197-205, March.
    11. Tze Leung Lai & Dylan Small, 2007. "Marginal regression analysis of longitudinal data with time‐dependent covariates: a generalized method‐of‐moments approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 79-99, February.
    12. Patrick J. Heagerty, 2002. "Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data," Biometrics, The International Biometric Society, vol. 58(2), pages 342-351, June.
    13. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
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