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

Authors:
Renan Moritz Varnier R. Almeida;Antonio Fernando C. Infantosi;José Hermógenes R. Suassuna;João Carlos G. D. Costa
Affiliations:
Biomedical Engineering Program - COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro, RJ 21941-972, Brazil;Biomedical Engineering Program - COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro, RJ 21941-972, Brazil;Renal Unit, Pedro Ernesto University Hospital, Medical College, State University of Rio de Janeiro, Rio de Janeiro, RJ 22291-080, Brazil;Biomedical Engineering Program - COPPE, Federal University of Rio de Janeiro, P.O. Box 68510, Rio de Janeiro, RJ 21941-972, Brazil
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

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Abstract

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors. These models were compared to two other models, ordinarily built by backward and forward stepwise selection, which yielded, respectively, five and two predictors. After internal validation, the models performance statistics showed similar results. Likelihood ratio tests suggested that backward approach achieved a better fit than the forward modelling in both methods and the Vuong's non-nested test between backward-built models suggested that these were undistinguishable. We conclude that the tolerance distance, in combination with MCA, could be a feasible method for variable selection in logistic modelling, when there are several categorical predictors.