Biesheuvel C J, Vergouwe Y, Steyerberg E W, Grobbee D E, Moons K G M
Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.
J Clin Epidemiol. 2008 Feb;61(2):125-34. doi: 10.1016/j.jclinepi.2007.03.002. Epub 2007 Jun 29.
Physicians commonly consider the presence of all differential diagnoses simultaneously. Polytomous logistic regression modeling allows for simultaneous estimation of the probability of multiple diagnoses. We discuss and (empirically) illustrate the value of this method for diagnostic research.
We used data from a study on the diagnosis of residual retroperitoneal mass histology in patients presenting with nonseminomatous testicular germ cell tumor. The differential diagnoses include benign tissue, mature teratoma, and viable cancer. Probabilities of each diagnosis were estimated with a polytomous logistic regression model and compared with the probabilities estimated from two consecutive dichotomous logistic regression models.
We provide interpretations of the odds ratios derived from the polytomous regression model and present a simple score chart to facilitate calculation of predicted probabilities from the polytomous model. For both modeling methods, we show the calibration plots and receiver operating characteristics curve (ROC) areas comparing each diagnostic outcome category with the other two. The ROC areas for benign tissue, mature teratoma, and viable cancer were similar for both modeling methods, 0.83 (95% confidence interval [CI]=0.80-0.85) vs. 0.83 (95% CI=0.80-0.85), 0.78 (95% CI=0.75-0.81) vs. 0.78 (95% CI=0.75-0.81), and 0.66 (95% CI=0.61-0.71) vs. 0.64 (95% CI=0.59-0.69), for polytomous and dichotomous regression models, respectively.
Polytomous logistic regression is a useful technique to simultaneously model predicted probabilities of multiple diagnostic outcome categories. The performance of a polytomous prediction model can be assessed similarly to a dichotomous logistic regression model, and predictions by a polytomous model can be made with a user-friendly method. Because the simultaneous consideration of the presence of multiple (differential) conditions serves clinical practice better than consideration of the presence of only one target condition, polytomous logistic regression could be applied more often in diagnostic research.
医生通常会同时考虑所有鉴别诊断。多分类逻辑回归建模允许同时估计多种诊断的概率。我们讨论并（通过实证）说明这种方法在诊断研究中的价值。
我们使用了一项关于非精原性睾丸生殖细胞肿瘤患者残余腹膜后肿块组织学诊断研究的数据。鉴别诊断包括良性组织、成熟畸胎瘤和存活癌。使用多分类逻辑回归模型估计每种诊断的概率，并与两个连续的二分类逻辑回归模型估计的概率进行比较。
我们对多分类回归模型得出的比值比进行了解释，并给出了一个简单的评分表，以方便从多分类模型计算预测概率。对于两种建模方法，我们展示了校准图以及将每个诊断结果类别与其他两个类别进行比较的受试者工作特征曲线（ROC）面积。两种建模方法中，良性组织、成熟畸胎瘤和存活癌的ROC面积相似，多分类回归模型和二分类回归模型的相应面积分别为0.83（95%置信区间[CI]=0.80 - 0.85）对0.83（95% CI=0.80 - 0.85）、0.78（95% CI=0.75 - 0.81）对0.78（95% CI=0.75 - 0.81）以及0.66（95% CI=0.61 - 0.71）对0.64（95% CI=0.59 - 0.69）。
多分类逻辑回归是一种用于同时对多种诊断结果类别的预测概率进行建模的有用技术。多分类预测模型的性能可以与二分类逻辑回归模型类似地进行评估，并且可以使用一种用户友好的方法进行多分类模型的预测。由于同时考虑多种（鉴别）情况比仅考虑一种目标情况更符合临床实践，多分类逻辑回归在诊断研究中可以更频繁地应用。
