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Multiple imputation methods for handling missing values in longitudinal studies with sampling weights: Comparison of
Anurika P De Silva1, Alysha M De Livera1, Katherine J Lee2,3
1Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
Biometrical Journal. Biometrische Zeitschrift
|October 26, 2020
Summary
For longitudinal studies with missing data, multivariate normal imputation (MVNI) is recommended over fully conditional specification (FCS) when using sampling weights. MVNI with design stratum or sampling weight as a covariate offers minimal bias and good coverage.
Area of Science:
- Biostatistics
- Epidemiology
- Longitudinal Data Analysis
Background:
- Longitudinal cohort studies often require both sampling weights and multiple imputation (MI) for accurate analysis.
- Current methodologies lack clear guidance on integrating MI with sampling weights.
Purpose of the Study:
- To evaluate the performance of different multiple imputation (MI) methods when combined with sampling weights in longitudinal studies.
- To provide recommendations for best practices in handling missing data and sampling weights in such analyses.
Main Methods:
- Simulated a target population and drew samples mimicking the Longitudinal Study of Australian Children.
- Compared multivariate normal imputation (MVNI), fully conditional specification (FCS), and inverse probability weighting (IPW) approaches.
- Assessed methods based on bias, precision, and convergence issues, incorporating design stratum and sampling weights.
Main Results:
- FCS and twofold FCS methods exhibited severe convergence issues.
- MVNI-based approaches demonstrated minimal bias and nominal coverage, performing consistently.
- Inverse probability weighting (IPW) showed comparable bias to MVNI but lower precision.
Conclusions:
- Multivariate normal imputation (MVNI) is a reliable method for handling missing data with sampling weights in longitudinal studies.
- Recommends using MVNI with the design stratum as a covariate; if unavailable, including the sampling weight as a covariate is a suitable alternative.
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