Population ‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models

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 th at 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.
Source: Statistics in Medicine - Category: Statistics Authors: Tags: RESEARCH ARTICLE Source Type: research