Ho-Jin Lee, Hee-Yeon Suh, Yun-Sik Lee, Shin-Jae Lee, Richard E Donatelli, Calogero Dolce, Timothy T Wheeler
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.
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Bias
Cephalometry
Chin
Face
Facial Asymmetry
Female
Forecasting
Genioplasty
Humans
Least-Squares Analysis
Lip
Male
Malocclusion, Angle Class II
Mandibular Osteotomy
Maxillary Osteotomy
Models, Biological
Multivariate Analysis
Nose
Orthognathic Surgical Procedures
Osteotomy, Le Fort
Osteotomy, Sagittal Split Ramus
Overbite
Treatment Outcome
Young Adult
