Multiple imputation of missing data under missing at random: compatible imputation models are not su...
Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified
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United States: Elsevier Inc
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English
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United States: Elsevier Inc
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AbstractObjectiveEpidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). Standard (default) MI procedures use simple linear covariate functions in the imputation model. We examine the bias that may be caused by acceptance of this default option and evaluate methods to identify problematic imputation...
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Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7615471
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7615471
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ISSN
0895-4356
E-ISSN
1878-5921
DOI
10.1016/j.jclinepi.2023.06.011
