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A better statistical method of predicting postsurgery soft tissue response in Class II patients
Ho-Jin Lee1, Hee-Yeon Suh, Yun-Sik Lee
1a Graduate Student, Department of Orthodontics, Seoul National University School of Dentistry & Dental Research Institute, Seoul, Korea.
Objective:
To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients.
Materials And Methods:
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.
Results:
The multivariate PLS method provided a significantly more accurate prediction than the conventional OLS method.
Conclusion:
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.
