A comparative study of multiple imputation and subset correspondence analysis in dealing with missing data

Abstract

Methods: Multiple imputation and subset correspondence analysis are applied to a set of child asthma data that is mainly categorical and suffers from non-response. Differences in the methods and in the outcomes they produce are studied. In addition, the inclusion of interactions in a subset correspondence analysis is illustrated. Results: Despite the vast differences in the two approaches, they yielded similar results in the identification of genetic, environmental and socio-economic factors that affect childhood asthma. A number of exposure related variables were found to be associated with the greater severity of asthma. It was also found that a finer distinction between the asthma severity levels and their associations with factors was possible with a subset correspondence analysis, compared to the multiple imputation approach. Conclusions: Both multiple imputation and subset correspondence analysis were able to identify several factors associated with childhood asthma while at the same time successfully managing the missing data. This offers the researcher a choice to select the method that best suits his/her study.