Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables
Direct comparison of logistic regression and recursive partitioning to predict chemotherapy response of breast cancer based on clinical pathological variables
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DOI：
10.1007/s10549-009-0308-2
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发表时间：
2009-09-01
影响因子：
3.8
通讯作者：
Pusztai, Lajos
中科院分区：
文献类型：
--
作者：
Rouzier, Roman;Coutant, Charles;Pusztai, Lajos
目的是比较逻辑回归模型（LRM）和递归划分（RP）来预测乳腺癌患者对术前化疗的病理完全缓解。这两个模型是在 496 名患者的同一训练集中建立的，并在 337 名患者的同一验证集中进行验证。模型性能在区分度（通过接受者操作特征曲线 (AUC) 下的面积进行评估）和校准方面进行了量化。在训练集中，LRM 和 RP 模型的 AUC 相似（分别为 0.77（95% CI，0.74-0.80）和 0.75（95% CI，0.74-0.79）），而 LRM 在验证集中优于 RP（0.78（95% CI，0.74-0.82）对比 0.64（95% CI，0.74-0.82）） 0.60-0.67）。在这些真实数据集中，LRM 模型也优于 RP 模型，因此更适合临床使用。
The purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict pathologic complete response to preoperative chemotherapy in patients with breast cancer. The two models were built in a same training set of 496 patients and validated in a same validation set of 337 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves (AUC)) and calibration. In the training set, AUC were similar for LRM and RP models (0.77 (95% confidence interval, 0.74-0.80) and 0.75 (95% CI, 0.74-0.79), respectively) while LRM outperformed RP in the validation set (0.78 (95% CI, 0.74-0.82) versus 0.64 (95% CI, 0.60-0.67). LRM model also outperformed RP model in term of calibration. In these real datasets, LRM model outperformed RP model. It is therefore more suitable for clinical use.