Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study.

ABSTRACT: 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 (CCA), are generally inappropriate due to the loss of precision and risk of bias. Multiple imputation by fully conditional specification (FCS MI) is a powerful and statistically valid method for creating imputations in large data sets which include both categorical and continuous variables. It specifies the multivariate imputation model on a variable-by-variable basis and offers a principled yet flexible method of addressing missing data, which is particularly useful for large data sets with comp

SUBMITTER: Liu Y 

PROVIDER: S-EPMC4945131 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature