Statistical package for social sciences software version 17

All statistical analyses were performed using the Statistical Package for Social Sciences software, version 17.0 (SPSS, Inc., Chicago, IL, USA). Pearson’s chi-squared test or Fisher’s exact test was used to estimate the statistical significance between the categorized groups. The Kaplan-Meier curves were used to assess patient survival. Multivariable analyses of prognostic factors were performed using Cox’s proportional hazard regression model. All factors with univariate significance P values less than 0.05 were entered into a multivariable Cox model to estimate overall survival and lung cancer-specific survival. All statistical tests were two-sided and P value ≤ 0.05 was considered statistically significant.

The normal distribution of continuous variables was verified using the Kolmogorov–Smirnov test. Dichotomous variables are presented as absolute numbers and percentages. Continuous variables are presented as means and SD in the case of a normal distribution and medians and interquartile ranges in a non-normal distribution. The differences in proportions were determined using the chi-square test or Fisher’s exact test. Differences between groups with continuous variables were obtained using the Student’s t-test for variables with a normal distribution and the Mann–Whitney U-test for variables without a normal distribution. The survival graphs were prepared using the Kaplan–Meier method, and the difference between the survival curves was determined by the log-rank test. The size effect of the survival comparison was calculated using a simple Cox regression. A P-value <.05 was considered a statistically significant difference. The calculations were performed using Statistical Package for Social Sciences software version 17.0 (SPSS Inc.; Chicago, IL, USA). All P-values are 2-tailed.

In the statistical analysis, continuous variables were presented as means
± standard deviations or as medians and interquartile ranges when data
did not follow a Gaussian distribution. Categorical variables were presented as
proportions.
Continuous variables were analyzed using Student's t-test or non-parametric
Mann-Whitney test; categorical variables were analyzed using Fisher's exact test
or the chi-squared test, when appropriate. Parameters that presented a
difference between the groups with P<0.05 in the univariate
analysis were included in the multivariate analysis, which was performed using a
logistic regression model (backward Wald) to identify independent markers for
complicated evolution. The logistic regression results were described as
odds ratio (OR) and at a 95% confidence interval (95%
CI).
Receiver operating characteristic (ROC) curves of parameters identified as
complicated evolution predictors were constructed to find the best cut-off
points associated with complicated evolution. The cut-off point was determined
as the value associated with the highest sum of sensitivity and specificity.
Areas under the ROC curve were determined and compared. The analysis was
performed using the Statistical Package for Social Sciences software, version
17.0. A P-value < 0.05 was considered statistically
significant.

Continuous variables are shown as the mean ± SD, and all statistical analyses were performed using Statistical Package for Social Sciences software version 17.0 (Chicago, IL, USA). Volumetric data were assessed using an unpaired Student’s t-test, and one-way ANOVA followed by post hoc LSD test or Dunnett’s test was used for multiple comparisons. Concordance and correlation between AMF and GPER-1 were assessed with chi-square tests. Survival curves were assessed using a standard log-rank test and the Kaplan–Meier method. P values< 0.05 were considered statistically significant. All experiments were repeated independently at least three times.