Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

By Jonathan W. Bartlett, S. Seaman, Ian R. White, James R. Carpenter. Statistical Methods in Medical Research, 2012

463 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a5345e7763453c3d576c07ac4506163f89b04c0e

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Citations in the corpus

Multiple imputation as a flexible tool for missing data handling in clinical research. (2017, background): “Fortunately, promising new imputation procedures are currently under development and will likely become available in software packages in the near future (Bartlett et al., 2015).”
Multiple Imputation of Missing Data for Multilevel Models (2018, methodology): “With this procedure, Bartlett et al. (2015) demonstrated that the bias associated with nonlinear and interaction effects in single-level regression models can be greatly reduced (see also Goldstein, Carpenter, & Browne, 2014).”
Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study (2015, background): “continuous and some of which are categorical [26, 28].”
Maximum Likelihood Multiple Imputation: Faster Imputations and Consistent Standard Errors Without Posterior Draws (2012, unknown): “Here, the imputation and analysis models are compatible [7], and although they are unlikely to be perfectly specified, evidently any misspecification is not serious enough to introduce much bias.”