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      Multiple imputation for discrete data: Evaluation of the joint latent normal model

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          Abstract

          Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM‐MI) and full conditional specification multiple imputation (FCS‐MI). While JM‐MI is arguably a preferable approach, because it involves specification of an explicit imputation model, FCS‐MI is pragmatically appealing, because of its flexibility in handling different types of variables. JM‐MI has developed from the multivariate normal model, and latent normal variables have been proposed as a natural way to extend this model to handle categorical variables. In this article, we evaluate the latent normal model through an extensive simulation study and an application on data from the German Breast Cancer Study Group, comparing the results with FCS‐MI. We divide our investigation in four sections, focusing on (i) binary, (ii) categorical, (iii) ordinal, and (iv) count data. Using data simulated from both the latent normal model and the general location model, we find that in all but one extreme general location model setting JM‐MI works very well, and sometimes outperforms FCS‐MI. We conclude the latent normal model, implemented in the R package jomo, can be used with confidence by researchers, both for single and multilevel multiple imputation.

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          mice: Multivariate Imputation by Chained Equations inR

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            Bayesian Analysis of Binary and Polychotomous Response Data

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                Author and article information

                Contributors
                m.quartagno@ucl.ac.uk
                Journal
                Biom J
                Biom J
                10.1002/(ISSN)1521-4036
                BIMJ
                Biometrical Journal. Biometrische Zeitschrift
                John Wiley and Sons Inc. (Hoboken )
                0323-3847
                1521-4036
                14 March 2019
                July 2019
                : 61
                : 4 ( doiID: 10.1002/bimj.v61.4 )
                : 1003-1019
                Affiliations
                [ 1 ] Department of Medical Statistics London School of Hygiene and Tropical Medicine London UK
                [ 2 ] MRC Clinical Trials Unit at UCL 90 High Holborn London UK
                Author notes
                [*] [* ] Correspondence

                Matteo Quartagno, MRC Clinical Trials Unit at UCL, London, UK.

                Email: m.quartagno@ 123456ucl.ac.uk

                Author information
                https://orcid.org/0000-0003-4446-0730
                Article
                BIMJ2000
                10.1002/bimj.201800222
                6618333
                30868652
                92fbf0cc-face-41c7-a2aa-b6095d4f9f37
                © 2019 The Authors. Biometrical Journal Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 August 2018
                : 28 November 2018
                : 29 January 2019
                Page count
                Figures: 4, Tables: 5, Pages: 17, Words: 8953
                Funding
                Funded by: FP7 Health
                Award ID: 290025
                Funded by: Medical Research Council
                Award ID: MC UU 12023/21
                Award ID: MC UU 12023/29
                Categories
                Research Paper
                General Biometry
                Custom metadata
                2.0
                bimj2000
                July 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.5 mode:remove_FC converted:10.07.2019

                Quantitative & Systems biology
                categorical data,joint model,latent normal model,missing data,multiple imputation

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