Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified.

ABSTRACT:

Objectives

Epidemiological 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 models, providing practical guidance for researchers.

Study design and setting

Using simulation and real data analysis, we investigated how imputation model mis-specification affected MI performance, comparing results with complete records analysis (CRA). We considered scenarios in which imputation model mis-specification occurred because (i) the analysis model was mis-specified or (ii) the relationship between exposure and confounder wa

SUBMITTER: Curnow E 

PROVIDER: S-EPMC7615471 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature