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Multiple Imputation for Missing Values through Conditional Semiparametric Odds Ratio Models

Authors

  • Hua Yun Chen,

    Corresponding author
    1. Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois 60612, U.S.A.
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  • Hui Xie,

    Corresponding author
    1. Division of Epidemiology & Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois 60612, U.S.A.
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  • Yi Qian

    Corresponding author
    1. Department of Marketing, Kellogg School of Management, Northwestern University, Evanston, Illinois 60208, U.S.A.
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email:hychen@uic.edu

email:huixie@uic.edu

email:yiqian@northwestern.edu

Abstract

Summary Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging. We propose an imputation framework that uses conditional semiparametric odds ratio models to impute the missing values. The proposed imputation framework is more flexible and robust than the imputation approach based on the normal model. It is a compatible framework in comparison to the approach based on fully conditionally specified models. The proposed algorithms for multiple imputation through the Markov chain Monte Carlo sampling approach can be straightforwardly carried out. Simulation studies demonstrate that the proposed approach performs better than existing, commonly used imputation approaches. The proposed approach is applied to imputing missing values in bone fracture data.

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