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Impact of non-normal random effects on inference by multiple imputation: A simulation assessment
Recai M Yucel1, Hakan Demirtas
1Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, SUNY, One University Place Room 139, Rensselaer, NY 12144, United States.
Computational Statistics & Data Analysis
|June 8, 2010
Summary
Multiple imputation using multivariate linear mixed-effects models offers accurate regression coefficient estimates for incomplete data. However, misspecifying random-effect distributions can bias variance and association estimates.
Area of Science:
- Statistics
- Biostatistics
- Data Analysis
Background:
- Multivariate linear mixed-effects models are vital for analyzing multilevel incomplete data using multiple imputation.
- The normality assumption of error terms and random effects is critical for accurate imputation.
Purpose of the Study:
- To assess the plausibility of the normality assumption for subject-specific random effects.
- To investigate the performance of multiple imputation under a multivariate linear mixed-effects model with varying data characteristics.
Main Methods:
- Simulated incomplete data sets with diverse distributional properties.
- Analysis using multivariate linear mixed-effects models for multiple imputation.
- Assessment of regression coefficient, random-effect variance, and association measure estimation.
Main Results:
- Multiple imputation demonstrated well-calibrated procedures with negligible bias and accurate coverage rates for regression coefficients under moderate missing data.
- Estimation of random-effect variance and association measures was negatively impacted by random-effect distribution misspecification and the number of incompletely-observed variables.
- Adverse impacts included reduced coverage rates and increased biases in variance and association estimates.
Conclusions:
- Multivariate linear mixed-effects models provide reliable regression coefficient estimates in multiple imputation for incomplete data.
- Careful consideration of random-effect distribution assumptions is necessary to avoid biases in variance and association estimation.
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