Comparison of AIC, AUC, and ROC curves for logistic regression models.
- Hiro Takahashi (65723)
- Kimie Sai (616024)
- Yoshiro Saito (195372)
- Nahoko Kaniwa (616025)
- Yasuhiro Matsumura (187249)
- Tetsuya Hamaguchi (616026)
- Yasuhiro Shimada (616027)
- Atsushi Ohtsu (223901)
- Takayuki Yoshino (616028)
- Toshihiko Doi (616029)
- Haruhiro Okuda (616030)
- Risa Ichinohe (616031)
- Anna Takahashi (475164)
- Ayano Doi (616032)
- Yoko Odaka (616033)
- Misuzu Okuyama (616034)
- Nagahiro Saijo (616035)
- Jun-ichi Sawada (616036)
- Hiromi Sakamoto (212872)
- Teruhiko Yoshida (65725)
Publication date
August 2014
DOIAbstract
<p>(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 (%).</p
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