The Angle orthodontist · 2014
Testing a better method of predicting postsurgery soft tissue response in Class II patients: A prospective study and validity assessment.
Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Yoon Kyoung-Sik, Lee Ho-Jin, Lee Shin-Jae, Donatelli Richard E
Аннотация
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.
MATERIALS AND 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.
CONCLUSION: 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.
MATERIALS AND 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.
CONCLUSION: 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.