Spss statistics software package version 24

Descriptive analysis was used to describe the characteristics of the study population including total number and percentage. Mean and standard deviation (SD) were used to describe the age of the participants.
In order to assess the relationship between AS status and oral conditions, a logistic regression model was used to calculate the odds ratio (OR) and 95% confidence interval (CI) adjusted for age, gender, educational qualification level, smoking status, alcohol consumption, and body mass index. Statistical significance level was defined at p=0.05. All data were processed using the IBM SPSS Statistics Software Package, version 24.

Statistical analysis was carried out using the SPSS statistics software package version 24 (IBM, Armonk, NY, USA). Quantitative results were evaluated by Mann–Whitney and Wilcoxon signed-rank test. p values lower than 0.05 were considered statistically significant. The predictive capability (i.e., diagnostic performance) of each biomarker was investigated by means of the area under the ROC (Receiver-Operating Characteristics) curve (AUC). The ROC curve measures the accuracy of biomarkers when their expression is detected on a continuous scale, displaying the relationship between sensitivity (true-positive rate, y-axes) and 1-specificity (false-positive rate, x-axes) across all possible threshold values of the considered biomarker. A useful way to summarize the overall diagnostic accuracy of the biomarker is the area under the ROC curve (AUC), the value of which is expected to be 0.5 in the absence of predictive capability, whereas it tends to be 1.00 in the case of high predictive capacity [31 (link)].