Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
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Abstract
Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, 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. Stata software implementing the approach is freely available.
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The record
- Venue
- Statistical Methods in Medical Research
- Topic
- Statistical Methods and Bayesian Inference
- Field
- Mathematics
- Canadian institutions
- —
- Funders
- National Institute on AgingEconomic and Social Research CouncilUniversity of California, San DiegoNational Institutes of HealthGenentechIXICONational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, Los AngelesServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaAlzheimer's AssociationAmorfix Life SciencesF. Hoffmann-La RocheMedpaceAstraZenecaEli Lilly and CompanyBristol-Myers SquibbNovartis Pharmaceuticals CorporationSynarcBayer HealthCareAlzheimer's Disease Neuroimaging InitiativeMedical Research CouncilMeso Scale DiagnosticsFoundation for the National Institutes of Health
- Keywords
- CovariateImputation (statistics)Missing dataEconometricsComputer scienceStatisticsSpecificationMathematics
- Has abstract in OpenAlex
- yes