Figure_4.tif (606.6 kB)
Comparison of AIC, AUC, and ROC curves for logistic regression models.
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posted on 2014-08-15, 03:01 authored by Hiro Takahashi, Kimie Sai, Yoshiro Saito, Nahoko Kaniwa, Yasuhiro Matsumura, Tetsuya Hamaguchi, Yasuhiro Shimada, Atsushi Ohtsu, Takayuki Yoshino, Toshihiko Doi, Haruhiro Okuda, Risa Ichinohe, Anna Takahashi, Ayano Doi, Yoko Odaka, Misuzu Okuyama, Nagahiro Saijo, Jun-ichi Sawada, Hiromi Sakamoto, Teruhiko Yoshida(A) Parameters of each model. (B) The ROC curve of a model consisting of rs9351963+MMC+ Amrubicin. ROC: receiver operating characteristic, AUC: area under the ROC curve, NULL indicates the model without parameters. Each genetic factor conforms to the proportional odds model, AIC: Akaike's information criterion, AUC: area under the ROC curve, Sens.: Sensitivity (%), Spec.: Specificity (%).
