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Practical strategies for handling breakdown of multiple imputation procedures
Cattram D Nguyen1,2, John B Carlin3,4, Katherine J Lee3,4
1Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, The Royal Children's Hospital, Flemington Road, Parkville, Victoria, 3052, Australia. cattram.nguyen@mcri.edu.au.
Emerging Themes in Epidemiology
|April 2, 2021
Summary
Multiple imputation, a method for incomplete data, can fail due to numerical issues in complex models. This study addresses common causes of failure and offers practical solutions for robust data analysis.
Area of Science:
- Statistics
- Data Science
- Biostatistics
Background:
- Multiple imputation is a standard technique for addressing missing data in statistical analyses.
- Failures in multiple imputation procedures, often caused by numerical issues, hinder its effective application.
- Complex models, particularly multivariate imputation by chained equations, are prone to such breakdowns.
Purpose of the Study:
- To identify common causes of multiple imputation procedure failure.
- To provide practical strategies for overcoming numerical problems in imputation models.
- To illustrate these strategies using a real-world dataset.
Main Methods:
- Analysis of common failure modes in multiple imputation algorithms, including perfect prediction and collinearity.
- Focus on issues encountered when using Stata statistical software.
- Development and application of strategies such as imputing composite variables, incorporating prior information, and modifying model forms.
Main Results:
- Identified perfect prediction and collinearity as frequent causes of imputation failure.
- Demonstrated the effectiveness of proposed strategies in resolving numerical issues.
- Successfully applied imputation techniques to data from the Longitudinal Study of Australian Children.
Conclusions:
- Understanding common failure points is crucial for successful multiple imputation.
- Implementing strategies like composite variable imputation and model modification enhances the robustness of the procedure.
- These methods provide a reliable framework for handling incomplete data in complex statistical analyses.
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