Jmp pro 15

Associations between clinical characteristics and the treatment response to afatinib were analyzed using Fisher's exact test or the chi-square test. Survival analysis was performed using Kaplan–Meier estimation to assess differences between the groups. Statistical significance was set at P ≤ 0.05. All statistical analyses were performed using JMP Pro 15.0 (SAS Institute Inc.).

Statistical analyses were performed using Student's t‐test, the chi‐square test, or Fisher's exact test for categorical data, and the Mann–Whitney U‐test for nonparametric data. Correlations were assessed by calculating the Pearson's correlation coefficient. p < 0.05 was considered statistically significant. Patient survival was examined with the Kaplan–Meier method, and the log‐rank test was used to determine significance. Data are shown as the mean ± SD of the indicated number of experiments. Statistical analyses were performed using JMP Pro 15.0 (SAS Institute Inc.).

JMP Pro 15.0 (SAS) was used to process participant behavioral data. To reduce the dimensionality of our statistical model and address collinearity among model predictors (i.e., mitigate the effect of reduced statistical significance due to collinearity between skill retention and visuospatial test scores), principal component analysis (PC) was used to create a ‘composite score’ that represented the shared variance of skill retention and Delayed Recall score for each participant. Since our previous work has shown a relationship between these two variables [11 (link), 28 (link)], the PC analysis allowed for consideration of only the shared variance between them as an independent variable. Only PCs with an eigenvalue greater than one were carried forward in subsequent analyses.
Using MATLAB 2020 (MathWorks, Inc.), significant PCs and age (a covariate of noninterest) were entered into a general linear model that was applied at each voxel for each diffusion map and an FDR-correction was applied to account for multiple statistical tests. Clusters were defined as at least 100 contiguous voxels where the FDR corrected p-value was < 0.01; clusters were transformed from template space to MNI using antsApplyTransforms (ANTs) and the Johns Hopkins University JHU atlas [38 (link), 39 (link)] was used to identify the neuroanatomical location of each cluster.

Continuous data (variables) were expressed as mean ± standard deviation (SD) or median (interquartile range). Categorical variables were presented as number of patients (percentage). Differences between patients with and without significant TR were compared with Student’s t-test or the Wilcoxon rank-sum test as appropriate. For categorical variables, Pearson’s chi-square test was performed. Univariable and multivariable logistic regression analyses were used to identify the determinants of significant TR, and odds ratios (ORs) along with 95% confidence intervals (CI) reported. Those with a p-value < 0.05 on univariable analysis were selected as independent variables for multivariable analysis. To clarify the best indexation of TV parameters for significant atrial FTR, receiver operating characteristic (ROC) curves were constructed to evaluate and compare the area under the curve (AUC). Two-sided p-values < 0.05 were considered to indicate statistical significance. JMP Pro 15.0 (SAS Institute, NC, USA) was used for all statistical analyses.