Spss 23.0 statistical package

Differences in the distributions of demographic characteristics, selected variables, genotypes of the PADI4 variants, and the correlation between genotyping and pathologic state were evaluated using the χ² test. The associations between the seven SNPs and risk of ESCC were estimated by computing the odds ratios (ORs) and their 95% confidence intervals (CIs) using logistic regression analyses for crude ORs and adjusted ORs when adjusting for age, sex, smoking and drinking status. The HWE was tested by a goodness-of-fit χ² test to compare the observed genotype frequencies to the expected frequencies among the control subjects. The Bonferroni correction procedure was applied because of the number of comparisons. As multiple hypotheses are tested, the chance of a rare event increases, and the likelihood of incorrectly rejecting a null hypothesis (type I error) increases, the Bonferroni correction was therefore performed. All statistical analyses were performed with SPSS 23.0 Statistical Package (SPSS Inc., Chicago, IL).

Statistical analyses were performed using the SPSS 23.0 statistical package (SPSS, Inc., Chicago, IL, USA). Quantitative variables were expressed as mean ± standard deviation (SD) or medians with interquartile ranges (IQR). The chi-square test and Fisher’s exact test were used to compare categorical variables; the t-test or Mann–Whitney U-test were used to compare continuous variables. The overall survival (OS) relationships between groups were analyzed using Kaplan–Meier survival curves and the log-rank test. A two-tailed p-value of < 0.05 was considered as statistically significant.

A statistical analysis of basic clinical information was performed using SPSS software (SPSS 23.0 statistical package; SPSS Inc., Armonk, NY, USA: IBM Corp.). A chi-squared test was performed to determine significant differences in sex and tumor type between the two groups. Differences in the age distribution were evaluated using Student’s t-test. A p-value < 0.05 was considered statistically significant.

Continuous variables were presented as the mean ± standard deviation if they were normally distributed or as the median and interquartile range if they had a skewed distribution. Categorical variables were presented as absolute and relative frequencies. One-way analysis of variance test or the rank sum test was used to compare continuous variables, where appropriate, and an χ² test was used to compare categorical variables. All P-values were two-tailed, and the level of significance was set at 0.05. All calculations were performed using the SPSS 23.0 statistical package (SPSS, Chicago, IL, USA).

SPSS 23.0 statistical package (SPSS Inc., Chicago, IL, USA) and R 3.4.4 project were used for analysis (Bell Laboratories, Murray Hill, NJ, USA). The optimal cut-off value for poNLR was calculated by X-tile program (Yale University, New Haven, CT, USA). The correlations between poNLR and clinicopathologic variables were analyzed using Pearson's χ2 test or Fisher's exact test as appropriate. Log-rank test and Kaplan-Meier method were used to analyze and depict survival curves. Independent prognostic factors were identified by using Cox proportional hazards regression model for multivariate analysis, and significant difference was found when p value was less than 0.05.
Novel prognostic nomograms for OS and RFS based on poNLR and AJCC 8th staging system were respectively established. Concordance index(C-index), calibration curve and decision curve analysis (DCA) were further used to evaluated predictive performance as previously described3 (link), 19 (link), 20 (link).

The prevalence and distribution of alveolar bone dehiscence and fenestration on the labial side of the upper and lower anterior teeth were determined. Alveolar bone dehiscence and fenestration were graded according to the d and f values, respectively. A 2 < d ≤ 4 mm was considered mild alveolar bone dehiscence; a 4 < d ≤ 6 mm was considered moderate dehiscence; and a d > 6 mm was considered severe dehiscence. A 2.2 < f ≤ 4 mm was considered mild fenestration; a 4 < f ≤ 6 mm was considered moderate fenestration; and an f > 6 mm was considered severe fenestration.
The distributions of the severity of alveolar bone dehiscence and fenestration were determined separately, and the number of bone dehiscence and fenestration present in the anterior teeth of each case was calculated.
Statistical analyses were performed using the SPSS 23.0 statistical package. Descriptive statistics are used to analyze the presence of dehiscence and fenestration in different teeth, as well as their correlation. Intraclass correlation coefficients (ICCs) were calculated to assess the intra-observer agreement. ICC estimates and their 95% confident intervals were calculated based on a single-rating ((intra-agreement)/mean-rating (inter-agreement)), absolute-agreement, 2-way mixed-effects model.