Nificant predictor of group membership (Wald 2 = 16.69, df = 1, p < .0001; = .061), with 71 of CWNS

Nificant predictor of group get Olumacostat glasaretil web Hexanoyl-Tyr-Ile-Ahx-NH2 membership (Wald 2 = 16.69, df = 1, p < .0001; = .061), with 71 of CWNS and 41 of CWS correctly classified based on the frequency of non-stuttered disfluencies. Moreover, the number of total disfluencies was a significant predictor of group membership (Wald 2 = 111.99, df = 1, p < .0001; = .263), with 91.4 of CWNS and 85 of CWS correctly classified based on the frequency of total disfluencies. Both classification tables are in Tables 4 and 5.J Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Page3.5. Follow-up analysis: sensitivity and specificity analysis for non-stuttered and total disfluenciesNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptBased on the finding that the number of non-stuttered disfluencies, as well as the number of total disfluencies, were significant predictors of group membership, a receiver operating characteristic curve (ROC) analysis (see Swets, 1992) was used to select the optimal threshold to be used in talker group classification.6 This analysis was necessary because sensitivity (true positives) and specificity (true negatives) depend on the chosen cut point. Although the threshold criterion to classify children as stuttering or normally fluent based on frequency of their disfluencies potentially can be set anywhere along the continuous scale of disfluency frequency, in the present study we selected what is known to be a "strict approach" (Swets, 1992). This approach sets a criterion that yields few false positive classifications (i.e., classifying someone as a CWS when, in fact, he or she is normally fluent). Hence we set our criterion to yield false positive classifications at .05 (similar to hypothesis testing procedures in which alpha is set to .05 or less). Thus, with specificity of .95, the 7 non-stuttered disfluencies criterion was identified as a threshold for CWS classification. The area under the ROC curve, a measure of strength of predictive capacity of the model over all cut points, for non-stuttered disfluencies was .61. This was better than chance (.50) but far from perfect (1.00). This indicated that the model fits moderately well and has fair discriminatory ability (Petrie Sabin, 2009), however the sensitivity of this model is relatively low (11 ). The sensitivity pecificity analysis for nonstuttered disfluencies is presented in Table 6. This table shows the trade-off between sensitivity and specificity. For example, a cut point of 10 non-stuttered disfluencies produces high specificity (99 ) but low sensitivity (2 ). The same approach was adopted for sensitivity pecificity analysis for number of total disfluencies. With the specificity criterion set at .95 (yielding false positive classifications on the order of .05), 8 total disfluencies was identified as a threshold for CWS classification. The area under the ROC curve for total disfluencies was .958, suggesting a very strong discriminatory ability at almost any cutting score. The sensitivity pecificity analysis for total disfluencies is presented in Table 7. 3.6. Hypotheses 4: stuttered disfluencies and parental concern To determine the association between parental concern about their child's stuttering and examiner's judgment of stuttering based on frequency of stuttered disfluencies, we employed a logistic regression analysis. A talker group classification based solely on expressed parental concern about the child's stuttering was the dependent va.Nificant predictor of group membership (Wald 2 = 16.69, df = 1, p < .0001; = .061), with 71 of CWNS and 41 of CWS correctly classified based on the frequency of non-stuttered disfluencies. Moreover, the number of total disfluencies was a significant predictor of group membership (Wald 2 = 111.99, df = 1, p < .0001; = .263), with 91.4 of CWNS and 85 of CWS correctly classified based on the frequency of total disfluencies. Both classification tables are in Tables 4 and 5.J Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Page3.5. Follow-up analysis: sensitivity and specificity analysis for non-stuttered and total disfluenciesNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptBased on the finding that the number of non-stuttered disfluencies, as well as the number of total disfluencies, were significant predictors of group membership, a receiver operating characteristic curve (ROC) analysis (see Swets, 1992) was used to select the optimal threshold to be used in talker group classification.6 This analysis was necessary because sensitivity (true positives) and specificity (true negatives) depend on the chosen cut point. Although the threshold criterion to classify children as stuttering or normally fluent based on frequency of their disfluencies potentially can be set anywhere along the continuous scale of disfluency frequency, in the present study we selected what is known to be a "strict approach" (Swets, 1992). This approach sets a criterion that yields few false positive classifications (i.e., classifying someone as a CWS when, in fact, he or she is normally fluent). Hence we set our criterion to yield false positive classifications at .05 (similar to hypothesis testing procedures in which alpha is set to .05 or less). Thus, with specificity of .95, the 7 non-stuttered disfluencies criterion was identified as a threshold for CWS classification. The area under the ROC curve, a measure of strength of predictive capacity of the model over all cut points, for non-stuttered disfluencies was .61. This was better than chance (.50) but far from perfect (1.00). This indicated that the model fits moderately well and has fair discriminatory ability (Petrie Sabin, 2009), however the sensitivity of this model is relatively low (11 ). The sensitivity pecificity analysis for nonstuttered disfluencies is presented in Table 6. This table shows the trade-off between sensitivity and specificity. For example, a cut point of 10 non-stuttered disfluencies produces high specificity (99 ) but low sensitivity (2 ). The same approach was adopted for sensitivity pecificity analysis for number of total disfluencies. With the specificity criterion set at .95 (yielding false positive classifications on the order of .05), 8 total disfluencies was identified as a threshold for CWS classification. The area under the ROC curve for total disfluencies was .958, suggesting a very strong discriminatory ability at almost any cutting score. The sensitivity pecificity analysis for total disfluencies is presented in Table 7. 3.6. Hypotheses 4: stuttered disfluencies and parental concern To determine the association between parental concern about their child's stuttering and examiner's judgment of stuttering based on frequency of stuttered disfluencies, we employed a logistic regression analysis. A talker group classification based solely on expressed parental concern about the child's stuttering was the dependent va.