Statistic analysis. Logistic regression analyses were performed to determine the influence of the different parameters on the success rate of autotransplantation of canines. Univariate logistic regression analyses were carried out to identify the effect of each parameter separately on the success rate. Parameters with p<0.25 on univariate logistic regression or those thought to be important on logical and medical grounds were entered into a multivariate logistic regression model. Multivariate logistic regression models were fitted in order to analyse the relative involvement of the various parameters in the success rate of autotransplantation. To compare the models in their goodness of fit, Akaike and Bayesian information criteria were used. Fitting the models was performed on complete observations. Mixed models were used to take into account correlation because some patients had more than one tooth transplanted. In order to estimate the inter-examiner variation all radiographs were evaluated by a second examiner. Simple κ statistics were computed to determine the inter-examiner agreement. Xxx Xxxxxx-Xxxxx method was used to estimate the survival probability of autogenously transplanted canines over a period of 14 years. Statistical analyses were performed with SAS (version 9.2) and S-plus (version 6.2). Significance was defined as p<0.05.
Statistic analysis. A paired t-test was performed to determine if there was a significant difference in surface distance and segmentation time between the different segmentation methods. Values were reported as the mean ± SD. Differences in mean values were considered significant if p-value < 0.05. Analyses were performed with StatView, version 5.0.