Jmp software for windows

Qualitative variables were analyzed using the chi-square test. Quantitative variables were analyzed using the one-way analysis of variance (ANOVA) to compare the differences between two or more groups. Comparisons between multiple groups were performed using analysis of variance with a post-hoc pairwise t-test. Correlations between CCR7 expression and the number of tumor buds were evaluated using Spearman’s rank method. Overall survival (OS) was calculated using the median time between the date of surgery and the date of the last follow-up or death. The cut-off value of the CCR7 staining H score for discriminating postoperative OS was obtained using a recursive partitioning technique. Kaplan–Meier estimates of OS curves were compared using the log-rank test. In the multivariate analysis of OS, the significance of prognostic factors was investigated using Cox proportional hazards models. The significance level was set to p < 0.05, and the confidence interval was 95%. Statistical analysis was performed using JMP software for Windows (version 14.0; SAS Institute, Inc., Cary, NC, USA).

McNemar’s test was used to evaluate differences between UCOD proportions estimated based on data solely from the death certificates and those estimated based on reference standard data. We also calculated the 95% Wald confidence intervals (CIs) with Bonett-Price Laplace adjustment for differences between proportions.^{18 (link)} Multivariate unconditional logistic regression analyses assessed the effect of age at death (<80 vs 80–89 and ≥90 years), sex, comorbidity, and major UCODs identified on death certificates (cancer, heart disease, pneumonia, and others) on UCOD misclassification. Comorbidity was defined as the number of clinical findings present among the 26 findings registered in the GEAD. In the logistic regression model, we had classified the number of comorbidity into three groups: no or low comorbidity (0–1 finding), moderate comorbidity (2–4 findings), and high comorbidity (≥5 findings).
Sensitivity and specificity with 95% Clopper-Pearson exact CIs were calculated for UCODs estimated to be present in at least 5% of the study population. We used SAS and JMP software for Windows (versions 9.3 and 10, respectively; SAS Institute, Cary, NC, USA) for all statistical analyses. Statistical significance was set at P < 0.05.

The data were analysed using JMP software for Windows (version10.0.2, SAS Institute Inc., Cary, NC). Results are presented as mean ± standard error of the mean (SEM). Comparisons between two groups were made with the unpaired t-test unless otherwise noted. Mann-Whitney U test was used for non-normal distributions. Comparisons between more than 2 groups were made with one-way or two-way repeated analysis of variance (ANOVA) followed by Tukey’s test as post hoc. A p-value of less than 0.05 was considered significant.

The data were processed using JMP software for Windows (version10.0.2, SAS Institute Inc., Cary, NC). Comparisons between two groups were made with the unpaired t-test. Comparisons between >2 groups were made with one-way or two-way repeated analysis of variance (ANOVA) followed by Tukey’s test as post hoc. Single regression analyses were performed to evaluate correlation between 2 values. Values are shown as mean ± SD. P values < 0.05 were considered significant.

Statistical analyses were performed in JMP software for Windows (version 8.0.1, SAS institute Inc, Cary, NC). Descriptive statistics were performed, and paired changes over time were analyzed using Wilcoxon signed-rank test (not normally distributed data). Association analyzes were performed using logistic regression, ANOVA test, and Pearson correlation analysis, according to categorical or continuous data.
P-values < 0.05 were considered statistically significant.

Normally distributed data were expressed as mean ± standard deviation; other values were reported as a median and interquartile range (IQR). We conducted the goodness-of-fit test and used the coefficient of determination as a measure to assess the significant relationships between the predictive curves and actual Kaplan–Meier curves of the cardiovascular event-free rate. The differences in the predictive curves were tested using the Wilcoxon signed-rank test. We estimated the error bounds of the parameters, α and β, by applying the standard bootstrap sampling¹⁶ . All tests were two-tailed, and P < 0·05 was considered significant. All analyses were performed using the JMP software for Windows (version 8.0.2, SAS Inc., Cary, NC).