An exploration of fixed and random effects selection for longitudinal binary outcomes in the presence of nonignorable dropout

Authors

  • Ning Li,

    Corresponding author
    • Department of Biomathematics, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
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  • Michael J. Daniels,

    1. Department of Statistics, University of Florida, Gainesville, FL, USA
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  • Gang Li,

    1. Department of Biostatistics, School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA
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  • Robert M. Elashoff

    1. Department of Biomathematics, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
    2. Department of Biostatistics, School of Public Health, University of California at Los Angeles, Los Angeles, CA, USA
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Corresponding author: e-mail: nli@biomath.ucla.edu, Phone: +1-310-794-7322, Fax: +1-310-267-2611

Abstract

We explore a Bayesian approach to selection of variables that represent fixed and random effects in modeling of longitudinal binary outcomes with missing data caused by dropouts. We show via analytic results for a simple example that nonignorable missing data lead to biased parameter estimates. This bias results in selection of wrong effects asymptotically, which we can confirm via simulations for more complex settings. By jointly modeling the longitudinal binary data with the dropout process that possibly leads to nonignorable missing data, we are able to correct the bias in estimation and selection. Mixture priors with a point mass at zero are used to facilitate variable selection. We illustrate the proposed approach using a clinical trial for acute ischemic stroke.

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