Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 24, Issue 4, Pages 462-487Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280214521348
Keywords
multiple imputation; compatibility; non-linearities; interactions; rejection sampling; fully conditional specification
Categories
Funding
- ESRC [RES-189-25-0103, RES-063-27-0257]
- MRC [G0900724]
- Medical Research Council [MC_US_A030_0015, U105260558]
- ADNI (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Canadian Institutes of Health Research
- NIH [P30 AG010129, K01 AG030514]
- ESRC [ES/H022252/1] Funding Source: UKRI
- MRC [MC_U105260558, MC_EX_G0800814, MR/K02180X/1, G0900724] Funding Source: UKRI
- Economic and Social Research Council [ES/H022252/1] Funding Source: researchfish
- Medical Research Council [MC_U105260558, MC_EX_G0800814, MC_PC_13041, MR/K02180X/1, G0900724, MR/K006584/1] Funding Source: researchfish
Ask authors/readers for more resources
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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