A heuristic index for selecting similar categories in multiple correspondence analysis applied to living donor kidney transplantation

Authors:
João Carlos G. D. Costa;Renan Moritz Varnier R. Almeida;Antonio Fernando C. Infantosi;José Hermógenes R. Suassuna
Affiliations:
Biomedical Engineering Program-COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro ZIP Code 21941-972, RJ, Brazil;Biomedical Engineering Program-COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro ZIP Code 21941-972, RJ, Brazil;Biomedical Engineering Program-COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro ZIP Code 21941-972, RJ, Brazil;Renal Unit, Pedro Ernesto University Hospital, Medical College, State University of Rio de Janeiro, Rio de Janeiro ZIP Code 22291-080, RJ, Brazil
Venue:
Computer Methods and Programs in Biomedicine
Year:
2008

Citing 4
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Multiple correspondence analysis in predictive logistic modelling: Application to a living-donor kidney transplantation data

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Abstract

This work introduces a heuristic index (the ''tolerance distance'') to define the ''closeness'' of two variable categories in multiple correspondence analysis (MCA). This index is a weighted Euclidean distance where weightings are based on the ''importance'' of each MCA axis, and variable categories were considered to be associated when their distances were below the tolerance distance. This approach was applied to a renal transplantation data. The analysed variables were allograft survival and 13 of its putative predictors. A bootstrap-based stability analysis was employed for assessing result reliability. The method identified previously detected associations within the database, such as that between race of donors and recipients, and that between HLA match and Cyclosporine use. A hierarchical clustering algorithm was also applied to the same data, allowing for interpretations similar to those based on MCA. The defined tolerance distance could thus be used as an index of ''closeness'' in MCA, hence decreasing the subjectivity of interpreting MCA results.