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Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables
Yi Cao1, Heather Allore2,3, Brent Vander Wyk2
1Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
Statistics in Medicine
|October 11, 2022
Summary
Multiple imputation methods address missing patient data. For longitudinal studies, Fully Conditional Specification (FCS) methods offer efficient and accurate estimation, particularly FCS-Standard for simpler models.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Missing Data Methods
Background:
- Estimating relationships with incomplete patient data necessitates handling missing values.
- Multiple imputation (MI) is a key strategy, involving plausible value imputation.
- MI methods include joint modeling (JM) and fully conditional specification (FCS).
Conclusions:
- FCS-Standard is recommended for its balance of efficiency and accuracy in many longitudinal analyses.
- FCS-LMM-latent is a valid choice for complex multivariate multilevel models, despite higher computational cost.
- Careful selection of MI methods is crucial to avoid bias in longitudinal data analysis.
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