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29 March 2024
 
  » arxiv » 1210.6799

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Congenial multiple imputation of partially observed covariates within the fully conditional specification framework
Jonathan W. Bartlett ; Shaun R. Seaman ; Ian R. White ; James R. Carpenter ;
Date 25 Oct 2012
AbstractMissing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation (MI). The imputation of partially observed covariates is complicated if the model of interest is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of MI may impute covariates from models that are uncongenial with such models of interest. We show how imputation by full conditional specification, a popular approach for performing MI, can be modified so that covariates are imputed from models which are congenial with the model of interest. We investigate through simulation the performance of this proposal, and compare it to existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common models of interest, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. In contrast, so called passive imputation of non-linear or interaction terms generally results in inconsistent estimates of the parameters of the model of interest, while a recently proposed alternative approach, based on treating such terms as ’just another variable’, gives consistent results only for linear models and only if data are missing completely at random.
Source arXiv, 1210.6799
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