electronic health records, where it results in scientifically relevant changes in inference for non-White ethnic groups compared to standard MI. Calibrated-$\delta$ adjustment MI represents a pragmatic approach for utilising available population-level information in a sensitivity analysis to explore potential departure from the MAR assumption.'
author:
- 'Tra My Pham^$\star$^'
- James R Carpenter
- Tim P Morris
- Angela M Wood
- Irene Petersen
bibliography:
- 'ama.bib'
title: 'Population-calibrated multiple imputation for a binary/categorical covariate in categorical regression models'
---

Introduction {#sec1}
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Multiple imputation (MI) [@Rubin1987] has increasingly become a popular tool for analyses with missing data in medical research [@Sterne2009; @Klebanoff2008]; the method is now incorporated in many standard statistical software packages.[@StataCorp2015a; @VanBuuren2011; @Yuan2011] In MI, several completed datasets are created and in each, missing data are replaced with values drawn from an imputation model which is the Bayesian posterior predictive distribution of the missing data, given the observed data. Each completed dataset is then analysed using the substantive analysis model that would have been used had there been no missing data. This process generates several sets of parameter estimates, which are then combined into a single set of results using Rubin’s rules.[@Rubin1987; @Barnard1999] Given congenial specification of 