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Published on: April 28, 2022
Missing value imputation in longitudinal measures of alcohol consumption
Ulrike Grittner1, Gerhard Gmel, Samuli Ripatti
1Institute for Biometrics and Clinical Epidemiology, Charité – University Medicine Berlin, Germany. ulrike.grittner@charite.de
Summary
Longitudinal study attrition can bias results. The Bayesian imputation method proved most accurate for analyzing alcohol consumption data, even with complex data structures.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Longitudinal studies are susceptible to attrition bias, potentially skewing results.
- Unexpected decreases in alcohol consumption despite increased availability warrant investigation into attrition effects, particularly concerning heavy drinkers.
Purpose of the Study:
- To compare the performance of various missing data imputation techniques in longitudinal alcohol consumption studies.
- To identify the most effective imputation method for semi-continuous, non-normally distributed alcohol consumption data.
Main Methods:
- Compared five imputation techniques: Last Value Carried Forward (LVCF), Hotdeck, Heckman modelling, Multivariate Imputation by Chained Equations (MICE), and a Bayesian approach.
- Utilized predictive mean matching to address non-normality in continuous alcohol consumption data.
- Validated methods using a simulated dataset alongside data from a Danish longitudinal study (N=1771, 2003–2006).
Main Results:
- The Bayesian imputation approach demonstrated the most unbiased estimates in simulation analyses.
- Despite higher alcohol availability, the overall finding of no increase in consumption levels remained consistent across methods.
Conclusions:
- The Bayesian method is recommended for imputing missing alcohol consumption data in longitudinal studies due to its accuracy.
- Attrition bias did not alter the primary finding regarding alcohol consumption trends despite increased availability.
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