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Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid
Elinor Curnow1, James R Carpenter2, Jon E Heron1
1Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
Journal of Clinical Epidemiology
|June 21, 2023
Summary
Standard multiple imputation (MI) can introduce bias in epidemiological studies due to default linear covariate functions. Researchers can identify and correct problematic imputation models using proposed methods to ensure accurate results.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Missing data are common in epidemiological studies.
- Multiple imputation (MI) is a standard method for handling missing data.
- Default MI procedures often use simple linear covariate functions.
Purpose of the Study:
- To examine bias caused by default MI procedures.
- To evaluate methods for identifying problematic imputation models.
- To provide practical guidance for researchers.
Main Methods:
- Simulation and real data analysis were used.
- Investigated imputation model mis-specification effects on MI performance.
- Compared MI with complete records analysis (CRA).
Main Results:
- Mis-specification of relationships between outcome, exposure, or confounders can bias CRA and MI estimates.
- MI by predictive mean matching can mitigate model mis-specification.
- Methods for examining model mis-specification effectively identified problematic relationships.
Conclusions:
- Compatibility between analysis and imputation models is necessary but not sufficient to avoid bias in MAR data.
- A step-by-step procedure is proposed for identifying and correcting imputation model mis-specification.
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