Jmp 11 statistical software package

The association between the result of the single knee-up test of a patient and the results of AIS was investigated. Fisher's exact test was used to compare categorical data. Odds ratio (OR), and 95% confidence interval (CI), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The kappa statistic was used to assess the inter-observer agreement on the knee-up test.
To clarify the importance of the key leg muscles in maintaining the knee in its lifted position, the relationship between the result of the knee-up test and the strength of each key leg muscle was analysed using a multiple logistic regression model. Correlations between the motor scores of the significant key muscles and those of the other key muscles were analysed using Pearson's correlation coefficient. The correlation coefficient (r) was interpreted as follows: value of r between -1.0 and -0.7 or 1.0 and 0.7: a strong linear relationship; between -0.7 and -0.5 or 0.5 and 0.7: a moderate linear relationship; and between -0.5 and -0.3 or 0.3 and 0.5: a weak linear relationship [31] . All statistical analyses were performed using the JMP 11 statistical software package (SAS Institute Inc., Cary, NC, USA). A value of p < 0.05 was considered to be statistically significant.

Patient characteristics and preoperative echocardiographic findings shown in Table 1, operative characteristics in Table 2, and follow-up echocardiographic findings and postoperative characteristics in Table 3 were compared by univariate analyses using the x 2 test (for categoric variables) and the Mann-Whitney U test (for continuous variables).
Continuous variables are expressed as mean AE SD, and categorical variables as percentages. Freedom from late Af, survival and combined endpoint curves were obtained using the Kaplan-Meier method, and were compared between groups using the log-rank test. Occurrence of late Af was analyzed by censoring at time of death or last follow-up, and the time analyzed was that between surgery and occurrence of Af or last follow-up. The Cox hazard model was used to predict risk factors for late Af. Independent risk factors for late Af were identified by multivariate Cox proportional hazards analysis. Variables with a univariate p-value of <0.05 were entered into the multivariate model. A value of p < 0.05 was considered statistically significant. All statistical analyses were performed using the JMP 11 statistical software package (SAS Institute, Inc., Cary, NC, USA).