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Evaluation of software for multiple imputation of semi-continuous data
L-M Yu1, Andrea Burton, Oliver Rivero-Arias
1Cancer Research UK/NHS Centre for Statistics in Medicine, Wolfson College Annexe, Linton Road, Oxford, UK. ly-mee.yu@cancer.org.uk
Multiple imputation (MI) methods are preferred for missing data, but their performance varies for semi-continuous data. This study evaluated MI software, finding significant differences in handling data with many zeros.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- Multiple imputation (MI) is the standard for handling missing data uncertainty.
- Statistical software packages (SAS, R, STATA) offer MI procedures.
- Suitability of standard MI for semi-continuous data (high proportion of zeros) is unclear.
Purpose of the Study:
- To evaluate the performance of MI procedures in statistical packages for semi-continuous data.
- To assess if standard MI applications preserve data distribution for subsequent analysis.
Main Methods:
- A simulation study was conducted.
- Complete resource use data from 1060 clinical trial participants formed the simulation population.
- 500 bootstrap samples were generated with imposed missing data.
Main Results:
- Significant differences were observed in the performance of MI programs when imputing semi-continuous data.
- The ability of MI procedures to preserve data distribution varied across software packages.
Conclusions:
- Caution is needed when selecting MI software for semi-continuous datasets.
- The choice of MI program impacts results for data with a large proportion of zero values.
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