Institute of Clinical Psychology and Psychotherapy, Technische Universitaet Dresden, Germany.
BMC Med Res Methodol. 2009 Sep 10;9:63. doi: 10.1186/1471-2288-9-63.
Epidemiological and clinical studies, often including anthropometric measures, have established obesity as a major risk factor for the development of type 2 diabetes. Appropriate cut-off values for anthropometric parameters are necessary for prediction or decision purposes. The cut-off corresponding to the Youden-Index is often applied in epidemiology and biomedical literature for dichotomizing a continuous risk indicator.
Using data from a representative large multistage longitudinal epidemiological study in a primary care setting in Germany, this paper explores a novel approach for estimating optimal cut-offs of anthropomorphic parameters for predicting type 2 diabetes based on a discontinuity of a regression function in a nonparametric regression framework.
The resulting cut-off corresponded to values obtained by the Youden Index (maximum of the sum of sensitivity and specificity, minus one), often considered the optimal cut-off in epidemiological and biomedical research. The nonparametric regression based estimator was compared to results obtained by the established methods of the Receiver Operating Characteristic plot in various simulation scenarios and based on bias and root mean square error, yielded excellent finite sample properties.
It is thus recommended that this nonparametric regression approach be considered as valuable alternative when a continuous indicator has to be dichotomized at the Youden Index for prediction or decision purposes.
流行病学和临床研究，通常包括人体测量学指标，已经确定肥胖是 2 型糖尿病发展的主要危险因素。为了预测或决策目的，人体测量参数的适当截断值是必要的。在流行病学和生物医学文献中，常常用约登指数（Youden Index）对应的截断值将连续的风险指标进行二分。
本文利用德国基层医疗环境中具有代表性的大型多阶段纵向流行病学研究的数据，在非参数回归框架中基于回归函数的不连续性，探索了一种用于预测 2 型糖尿病的人体形态参数的最优截断值的新方法。
所得截断值对应于基于约登指数（最大灵敏度和特异性之和减去一）得到的截断值，这在流行病学和生物医学研究中常被认为是最佳截断值。在各种模拟场景中，基于接收器工作特征（Receiver Operating Characteristic，ROC）图的既定方法和基于偏差和均方根误差的比较，基于非参数回归的估计器具有极好的有限样本特性。
因此，建议在需要将连续指标在约登指数处进行二分以用于预测或决策目的时，将这种非参数回归方法作为有价值的替代方法。
