Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the data inappropriately may bias study results, reduce power and efficiency, and alter important risk/benefit relationships. Standard ways of dealing with missing values, such as complete case analysis (...
| Main Authors: | , |
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| Format: | Online |
| Language: | English |
| Published: |
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945131/ |