Lee Ho-Jin, Suh Hee-Yeon, Lee Yun-Sik, Lee Shin-Jae, Donatelli Richard E, Dolce Calogero, Wheeler Timothy T
a Graduate Student, Department of Orthodontics, Seoul National University School of Dentistry & Dental Research Institute, Seoul, Korea.
Angle Orthod. 2014 Mar;84(2):322-8. doi: 10.2319/050313-338.1. Epub 2013 Aug 5.
To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients.
The subjects comprise 80 patients who had undergone surgical correction of severe Class II malocclusions. Using 228 predictor and 64 soft tissue response variables, we applied two multivariate methods of forming prediction equations, the conventional ordinary least squares (OLS) method and the partial least squares (PLS) method. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a leave-one-out cross-validation method was used.
The multivariate PLS method provided a significantly more accurate prediction than the conventional OLS method.
The multivariate PLS method was more satisfactory than the OLS method in accurately predicting the soft tissue profile change after surgical correction of severe Class II malocclusions.
提出一种更好的统计方法来预测Ⅱ类患者术后软组织反应。
研究对象包括80例接受严重Ⅱ类错牙合畸形手术矫治的患者。使用228个预测变量和64个软组织反应变量，我们应用了两种形成预测方程的多变量方法，即传统的普通最小二乘法（OLS）和偏最小二乘法（PLS）。拟合方程后，计算偏差和平均绝对预测误差。为评估预测方程的预测性能，采用留一法交叉验证。
多变量PLS方法提供的预测比传统OLS方法显著更准确。
在准确预测严重Ⅱ类错牙合畸形手术矫治后的软组织侧貌变化方面，多变量PLS方法比OLS方法更令人满意。
