A heuristic index for selecting similar categories in multiple correspondence analysis applied to living donor kidney transplantation.
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.
Costa
João Carlos G D
JC
Biomedical Engineering Program-COPPE, Federal University of Rio de Janeiro, Rio de Janeiro ZIP Code 21941-972, RJ, Brazil.
Almeida
Renan Moritz Varnier R
RM
Infantosi
Antonio Fernando C
AF
Suassuna
José Hermógenes R
JH
D000465:Algorithms; D016000:Cluster Analysis; D016572:Cyclosporine / Q000627:therapeutic use; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D006085:Graft Survival; D006650:Histocompatibility Testing; D006801:Humans; D007166:Immunosuppressive Agents / Q000627:therapeutic use; D016030:Kidney Transplantation / Q000276:immunology / Q000706:statistics & numerical data*; D019520:Living Donors / Q000706:statistics & numerical data*; D008297:Male; D015999:Multivariate Analysis; D012984:Software
D007166:Immunosuppressive Agents; D016572:Cyclosporine