Spss version 23

A two-step cluster analysis was performed using SPSS version 23.0 (Chicago, IL) to organize observations into two or more mutually exclusive groups, where members of the groups shared properties in common. Two clustering variables were used in the analysis: AS and EA based on ASI and AAQ scores, respectively. The log-likelihood distance measure was used to compute likelihood distance between clusters with subjects assigned to the cluster leading to the largest likelihood. Number of clusters was not predetermined. The Bayesian information criterion was used to judge adequacy of the final solution. Differences in sample demographic characteristics were compared according to cluster membership using univariate analysis of variance for continuous variables and χ² tests for categorical variables in order to characterize the resulting clusters. A multivariate analysis of covariance was conducted on outcome measures (DASS-SF, HAQ, and SF-36) according to cluster membership with patient demographic factors including age, gender, pain duration, and pain intensity as covariates. Post hoc analyses were conducted with a Tukey correction. SPSS version 23.0 (Chicago, IL) was used for all tests performed, with the significance level set at 0.05, two-tailed.

The statistical analysis was carried out using a completely randomized design (CRD). The conception and pregnancy rates were compared between groups via Chi-square analysis. The Chi-square test was statistically performed using the SPSS version 23.0 (SPSS Inc., Chicago, IL, USA). For biological parameters and immune and antioxidant activity levels, the gene expression and relative expression ratio were analyzed using one-way ANOVA. One-way analysis of variance (ANOVA) was also performed using SPSS version 23.0 (SPSS Inc., Chicago, IL, USA). Differences among the groups were analyzed using the new Duncan’s multiple range test (DMRT). Additionally, results are expressed as the mean ± SE.

OS was calculated using the Kaplan–Meier method. OS was defined as the time from the start day of first-line chemotherapy to death. Data on the survivors were censored at the last follow-up. The log-rank test was used for analysis of the differences between the survival curves. The comparison of the categorical variables between groups was performed by Fisher’s exact test. The Cox proportional hazards regression model was applied to determine the joint effects of several variables on survival and to assess interactions between treatment and subgroups in subgroup analyses. In the Cox proportional hazards regression model, factors with p values < 0.1 in univariate analysis were included. All statistical analyses were performed two-sided using SPSS version 23.0 for Windows.
Propensity score matching (PSM) was applied to reduce selection bias by balancing covariates that may be associated with the outcome. In the current study, the 1:1 nearest neighbor matching was performed using SPSS version 23.0 for Windows.

Statistical analysis was conducted by SPSS version 23 software. The continuous variables were displayed as mean ± standard deviation. Count variables were represented by n (%). A Student's t-test and χ² test were performed to evaluate the correlation between demographic and preoperative and postoperative characteristics. Binary logistic regression was used to analyse the risk factors of recurrent thyroid nodules after RFA treatment. Univariate analysis was used to obtain meaningful variables, and then the meaningful variables were included in binary regression model analysis to screen out the independent risk factors of recurrent thyroid nodules after RFA. The ability of each independent risk factor and combined model to predict thyroid recurrence was analysed by receiver Operating characteristic curve (ROC) analysis tool of SPSS version 23 software. The ROC curve is a curve reflecting the relationship between sensitivity and specificity. Area Under Curve (AUC), which is used to represent the prediction accuracy. The AUC value is proportional to the accuracy. It is generally believed that 0.5 0.9 indicates high predictive value. P <0.05 was considered a statistically significant difference. /span>

Data entry was done using Epi-Data version 3.1 after which the exported data were analyzed using SPSS version 23. The collected data were checked for completeness and consistency on a daily basis. Data qualitywas ensured by giving appropriate training to data collectors. Moreover, close supervision was also done during data collection for ensuring completeness and consistency. Double data entry was done to check accuracy. Data cleaning was performed by running a frequency distribution for each variable using SPSS version 23.