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    Model checking in multiple imputation: an overview and case study

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    25
    Author
    Nguyen, CD; Carlin, JB; Lee, KJ
    Date
    2017-08-23
    Source Title
    Emerging Themes in Epidemiology
    Publisher
    BMC
    University of Melbourne Author/s
    Lee, Katherine; Carlin, John; Nguyen, Cattram
    Affiliation
    Melbourne School of Population and Global Health
    Paediatrics (RCH)
    Metadata
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    Document Type
    Journal Article
    Citations
    Nguyen, C. D., Carlin, J. B. & Lee, K. J. (2017). Model checking in multiple imputation: an overview and case study. EMERGING THEMES IN EPIDEMIOLOGY, 14 (1), https://doi.org/10.1186/s12982-017-0062-6.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256803
    DOI
    10.1186/s12982-017-0062-6
    Abstract
    BACKGROUND: Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values. Despite the widespread use of multiple imputation, there are few guidelines available for checking imputation models. ANALYSIS: In this paper, we provide an overview of currently available methods for checking imputation models. These include graphical checks and numerical summaries, as well as simulation-based methods such as posterior predictive checking. These model checking techniques are illustrated using an analysis affected by missing data from the Longitudinal Study of Australian Children. CONCLUSIONS: As multiple imputation becomes further established as a standard approach for handling missing data, it will become increasingly important that researchers employ appropriate model checking approaches to ensure that reliable results are obtained when using this method.

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