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Multiple imputation of covariates in the presence of interactions and nonlinearities

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  • Jonathan Bartlett

    (London School of Hygiene and Tropical Medicine)

Abstract

Multiple imputation (MI) is a popular approach to handling missing data, and an extensive range of MI commands is now available in official Stata. A common problem is that of missing values in covariates of regression models. When the substantive model for the outcome contains nonlinear covariate effects or interactions, correctly specifying an imputation model for covariates becomes problematic. We present simulation results illustrating the biases that can occur when standard imputation models are used to impute covariates in linear regression models with a quadratic effect or interaction effect. We then describe a modification of the full conditional specification (FCS) or chained equations approach to MI, which ensures that covariates are imputed from a model which is compatible with a user-specified substantive model. We present the smcfcs Stata command, which implements substantive model compatible FCS and illustrate its application to a dataset.

Suggested Citation

  • Jonathan Bartlett, 2013. "Multiple imputation of covariates in the presence of interactions and nonlinearities," United Kingdom Stata Users' Group Meetings 2013 03, Stata Users Group.
    • Handle: RePEc:boc:usug13:03
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