Spss statistic version 21

The relationships between the lizards’ geographical variables, host specific variable and the infecting Leishmania species were analyzed using the Excel (Office 2016) and IBM SPSS Statistic (version 21.0). The 95% confidence interval to proportion (CI) was calculated as: upper limit of confidence interval, P + 1.96*SQTR(p(1-p)/n); lower limit of confidence interval, P-1.96*SQTR(p(1-p)/n). A Chi square test performed to demonstrate the relationships between species of Leishmania and specific characteristics. P value < 0.05 was established to determine statistical significance.

Descriptive statistics were computed for all variables. These include means and standard deviations or medians and 25^th and 75^th percentiles for continuous factors. For categorical variables, frequencies and percentages were calculated. Univariable regression analysis was used to examine association between two variables. Differences between groups were calculated by Mann-Whitney-U-rank test or the Kruskal-Wallis rank test. All tests were two-tailed, with a significant P value defined as <0.05. All data were analyzed using IBM SPSS Statistic Version 21.0 (Armonk, NY: IBM Corp.).

All statistical calculations were conducted using SPSS Statistic version 21.0 (SPSS Inc., Chicago, IL, USA) and R-project software version 3.5.3 (http://www.r-project.org/). Continuous data were presented as mean ± standard deviation and compared using the two-tailed Student’s t test or Wilcoxon test, while discontinuous variables were displayed as number (proportion) and compared using the χ² test or Fisher’s exact test. The OS and PFS curves were plotted using Kaplan–Meier method and compared using the log-rank test. Independent risk factors were identified using Cox proportional hazard regression models, and only those variables with a P value < 0.1 in the univariate analysis were enrolled into the multivariate analyses to predict postoperative survivals. Nomogram models were created using “rms” package of R software, based on the results of multivariate analysis. The cutoff values of HBx and CD68 were determined using time-dependent receiver operating characteristic (ROC) analysis with “survival ROC” package. The accuracy in predicting 5-year survival was assessed by the area under the curve (AUC) of ROC analysis. The discriminatory ability of any model or clinical indicators for survival prediction were calculated using Harrell’s concordance index (C-index) and calibration curves. A P value < 0.05 was considered statistically significant.

All the data were presented as mean ± standard deviation. The statistical analyses were carried out using IBM statistical product and service solutions (SPSS) Statistic Version 21.0 (SPSS; IBM, Armonk, NY, United States). One-way ANOVA test and two tailed Student’s t-test were used to analyze significant differences between the two groups. A p-value of less than 0.05 or 0.01 was considered statistical significance.

Statistical analyses were performed with the IBM SPSS Statistic version 21 (SPSS Inc., Chicago, IL). Linear associations between two continuous variables were evaluated by linear regression analysis and Spearman´s correlation. Univariate survival analyses were performed using the Kaplan-Meier method (log-rank significance test), and necrosis score was dichotomized based on the median. Associations between different categorical variables were assessed by Pearson's chi-square test. Wilcoxon Signed Rank test was used for comparing two related samples. Probability of < 0.05 was considered statistically significant.