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Population-calibrated Multiple Imputation for a Binary/categorical Covariate in Categorical Regression Models

Pham, TM; Carpenter, JR; Morris, TP; Wood, AM; Petersen, I; (2019) Population-calibrated Multiple Imputation for a Binary/categorical Covariate in Categorical Regression Models. Statistics in Medicine , 38 (5) pp. 792-808. 10.1002/sim.8004. Green open access

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Abstract

Multiple 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.

Type: Article
Title: Population-calibrated Multiple Imputation for a Binary/categorical Covariate in Categorical Regression Models
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.8004
Publisher version: https://doi.org/10.1002/sim.8004
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Electronic health records, missing data, missing not at random, multiple imputation, sensitivity analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Primary Care and Population Health
URI: https://discovery.ucl.ac.uk/id/eprint/10059903
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