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dc.contributor.authorPham, Tra My
dc.contributor.authorCarpenter, James R
dc.contributor.authorMorris, Tim P
dc.contributor.authorWood, Angela
dc.contributor.authorPetersen, Irene
dc.description.abstractMultiple imputation (MI) has become popular for analyses with missing data in medical research. The standard implementation of MI is based on the assumption of data being missing at random (MAR). However, for missing data generated by missing not at random mechanisms, MI performed assuming MAR might not be satisfactory. For an incomplete variable in a given data set, its corresponding population marginal distribution might also be available in an external data source. We show how this information can be readily utilised in the imputation model to calibrate inference to the population by incorporating an appropriately calculated offset termed the "calibrated-δ adjustment." We describe the derivation of this offset from the population distribution of the incomplete variable and show how, in applications, it can be used to closely (and often exactly) match the post-imputation distribution to the population level. Through analytic and simulation studies, we show that our proposed calibrated-δ adjustment MI method can give the same inference as standard MI when data are MAR, and can produce more accurate inference under two general missing not at random missingness mechanisms. The method is used to impute missing ethnicity data in a type 2 diabetes prevalence case study using UK primary care electronic health records, where it results in scientifically relevant changes in inference for non-White ethnic groups compared with standard MI. Calibrated-δ adjustment MI represents a pragmatic approach for utilising available population-level information in a sensitivity analysis to explore potential departures from the MAR assumption.
dc.description.sponsorshipTra My Pham was supported by the National Institute for Health Research (NIHR) School for Primary Care Research (project number 379) and awards to establish the Farr Institute of Health Informatics Research, London, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, NIHR, National Institute for Social Care and Health Research, and Wellcome Trust (grant MR/K006584/1). James Carpenter and Tim Morris were supported by the Medical Research Council (grant numbers MC_UU_12023/21 and MC_UU_12023/29).
dc.rightsAttribution 4.0 International
dc.subjectDiabetes Mellitus, Type 2
dc.subjectData Interpretation, Statistical
dc.subjectModels, Statistical
dc.subjectLogistic Models
dc.subjectResearch Design
dc.subjectEthnic Groups
dc.subjectElectronic Health Records
dc.titlePopulation-calibrated multiple imputation for a binary/categorical covariate in categorical regression models.
prism.publicationNameStat Med
dc.contributor.orcidPham, Tra My [0000-0003-0528-6303]
dc.contributor.orcidMorris, Tim P [0000-0001-5850-3610]
dc.contributor.orcidWood, Angela [0000-0002-7937-304X]
dc.contributor.orcidPetersen, Irene [0000-0002-0037-7524]
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (G0701619)
pubs.funder-project-idMedical Research Council (MR/K014811/1)
pubs.funder-project-idMedical Research Council (MR/L003120/1)
pubs.funder-project-idBritish Heart Foundation (None)

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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International