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Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates

Author

Listed:
  • Diana Miglioretti

    (Group Health Cooperative)

  • Patrick Heagerty

    (University of Washington)

Abstract

We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors; and a likelihood-based method implemented using Bayesian computational techniques. Implications of time-varying endogenous covariates are addressed. The methods are illustrated using data from the Breast Cancer Surveillance Consortium to estimate mammography accuracy from a repeatedly screened population.

Suggested Citation

  • Diana Miglioretti & Patrick Heagerty, 2004. "Marginal Modeling of Multilevel Binary Data with Time-Varying Covariates," UW Biostatistics Working Paper Series 1050, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1050
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    References listed on IDEAS

    as
    1. Nicole Mayer-Hamblett & Steve Self, 2001. "A Regression Modeling Approach for Describing Patterns of HIV Genetic Variation," Biometrics, The International Biometric Society, vol. 57(2), pages 449-460, June.
    2. Germán Rodríguez & Noreen Goldman, 2001. "Improved estimation procedures for multilevel models with binary response: a case‐study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 339-355.
    3. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
    4. Justine Shults & Ardythe L. Morrow, 2002. "Use of Quasi–Least Squares to Adjust for Two Levels of Correlation," Biometrics, The International Biometric Society, vol. 58(3), pages 521-530, September.
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    Cited by:

    1. Jonathan S. Schildcrout & Patrick J. Heagerty, 2007. "Marginalized Models for Moderate to Long Series of Longitudinal Binary Response Data," Biometrics, The International Biometric Society, vol. 63(2), pages 322-331, June.
    2. Loni Philip Tabb & Eric J. Tchetgen Tchetgen & Greg A. Wellenius & Brent A. Coull, 2016. "Marginalized Zero-Altered Models for Longitudinal Count Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 181-203, October.
    3. Keunbaik Lee & Michael J. Daniels, 2007. "A Class of Markov Models for Longitudinal Ordinal Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1060-1067, December.
    4. Lee, Keunbaik & Mercante, Donald, 2010. "Longitudinal nominal data analysis using marginalized models," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 208-218, January.
    5. Keunbaik Lee & Sanggil Kang & Xuefeng Liu & Daekwan Seo, 2011. "Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1577-1590, July.

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