title: Testing a better method of predicting postsurgery soft tissue response in Class II patients: A prospective study and validity assessment.
abstract: OBJECTIVE
  (1) To perform a prospective study using a new set of data to test the validity of a new soft tissue prediction method developed for Class II surgery patients and (2) to propose a better validation method that can be applied to a validation study.
  
  
  METHODS
  Subjects were composed of two subgroups: training subjects and validation subjects. Eighty Class II surgery patients provided the training data set that was used to build the prediction algorithm. The validation data set of 34 new patients was used for evaluating the prospective performance of the prediction algorithm. The validation was conducted using four validation methods: (1) simple validation and (2) fivefold, (3) 10-fold, and (4) leave-one-out cross-validation (LOO).
  
  
  RESULTS
  The characteristics between the training and validation subjects did not differ. The multivariate partial least squares regression returned more accurate prediction results than the conventional method did. During the prospective validation, all of the cross-validation methods (fivefold, 10-fold, and LOO) demonstrated fewer prediction errors and more stable results than the simple validation method did. No significant difference was noted among the three cross-validation methods themselves.
  
  
  CONCLUSIONS
  After conducting a prospective study using a new data set, this new prediction method again performed well. In addition, a cross-validation technique may be considered a better option than simple validation when constructing a prediction algorithm.
authors.affiliation: a  Graduate student, Department of Orthodontics, Seoul National University School of Dentistry, Seoul, Korea.
authors.forename: Kyoung-Sik
authors.initials: KS
authors.lastname: Yoon
authors.affiliation: a  Graduate student, Department of Orthodontics, Seoul National University School of Dentistry, Seoul, Korea.
authors.forename: Ho-Jin
authors.initials: HJ
authors.lastname: Lee
authors.affiliation: b  Professor and Chair, Department of Orthodontics, Seoul National University School of Dentistry, and Dental Research Institute, Seoul, Korea.
authors.forename: Shin-Jae
authors.initials: SJ
authors.lastname: Lee
authors.affiliation: c  Clinical Assistant Professor, Department of Orthodontics, University of Florida College of Dentistry, Gainesville, Fla.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Donatelli
mesh_terms: D000328:Adult; D000465:Algorithms; D059925:Anatomic Landmarks / Q000033:anatomy & histology; D002508:Cephalometry / Q000706:statistics & numerical data; D005145:Face / Q000033:anatomy & histology*; D005146:Facial Asymmetry / Q000601:surgery; D005260:Female; D005544:Forecasting; D063172:Genioplasty / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008312:Malocclusion, Angle Class II / Q000601:surgery*; D019186:Mandibular Advancement / Q000706:statistics & numerical data; D063166:Maxillary Osteotomy / Q000706:statistics & numerical data; D056948:Orthognathic Surgical Procedures / Q000706:statistics & numerical data*; D011446:Prospective Studies; D055815:Young Adult
keywords: Cross-validation; Prospective validation; Soft tissue prediction algorithm