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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 | Abstract: | Missing 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 | Services: | Forum | Review | PDF | Favorites |
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