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A Semi-Parametric Shared Parameter Model to Handle Nonmonotone Nonignorable Missingness

Authors

  • Roula Tsonaka,

    Corresponding author
    1. Biostatistical Centre, Catholic University of Leuven, U.Z. St. Rafaël, Kapucijnenvoer 35, B-3000 Leuven, Belgium
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  • Geert Verbeke,

    1. Biostatistical Centre, Catholic University of Leuven, U.Z. St. Rafaël, Kapucijnenvoer 35, B-3000 Leuven, Belgium
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  • Emmanuel Lesaffre

    1. Biostatistical Centre, Catholic University of Leuven, U.Z. St. Rafaël, Kapucijnenvoer 35, B-3000 Leuven, Belgium
    2. Department of Biostatistics, Erasmus MC, Dr. Molewaterplein 50, 3015 GE Rotterdam, The Netherlands
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email: spyridoula.tsonaka@med.kuleuven.be

Abstract

Summary Longitudinal studies often generate incomplete response patterns according to a missing not at random mechanism. Shared parameter models provide an appealing framework for the joint modelling of the measurement and missingness processes, especially in the nonmonotone missingness case, and assume a set of random effects to induce the interdependence. Parametric assumptions are typically made for the random effects distribution, violation of which leads to model misspecification with a potential effect on the parameter estimates and standard errors. In this article we avoid any parametric assumption for the random effects distribution and leave it completely unspecified. The estimation of the model is then made using a semi-parametric maximum likelihood method. Our proposal is illustrated on a randomized longitudinal study on patients with rheumatoid arthritis exhibiting nonmonotone missingness.

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