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Comparison of chi-squared automatic interaction detection classification trees vs TNM classification for patients
F Xavier Avilés-Jurado1, Ximena Terra, Enric Figuerola
1Department of Otorhinolaryngology-Head and Neck Surgery, Hospital Universitari de Tarragona Joan XXIII IISPV, Universitat Rovira i Virgili, Tarragona, Catalonia, Spain. fxavilesj@gmail.com
Objectives:
To compare chi-squared automatic interaction detection (CHAID) classification trees vs the seventh edition of the TNM classification for patients with head and neck squamous cell carcinoma and to assess whether CHAID classification trees might improve results obtained with the TNM classification.
Design:
Patient disease was classified according to CHAID classification trees and the TNM classification, and the results were compared.
Setting:
Academic research.
Patients:
A total of 3373 patients with carcinoma of the oral cavity, oropharynx, hypopharynx, or larynx.
Main Outcome Measures:
The 2 classification methods were evaluated objectively, measuring intrastage homogeneity (hazard consistency), interstage heterogeneity (hazard discrimination), and disease stage distribution among patients (balance). In addition, to assess agreement between CHAID classification trees and the TNM classification, we calculated the κ statistic, weighted linearly and quadratically.
Results:
Objective evaluation of the quality of the classification methods indicated that CHAID classification trees performed better than the TNM classification in terms of hazard consistency (2.51 for CHAID and 3.01 for TNM) and hazard discrimination (70.9% for CHAID and 52.7% for TNM) but not balance (-31.7% for CHAID and -15.5% for TNM). Analysis of concordance between the classification methods showed that the quadratic κ statistic was 0.77 (95% CI, 0.76-0.78) and the linear κ statistic was 0.59 (95% CI, 0.57-0.60) (P < .001 for both).
Conclusion:
CHAID classification trees performed better than the TNM classification and offer potential inclusion of new prognostic factors.
