Statistical Methods
The chi-squared test was used to compare continuous variables (rates)
and categorical variables (histological grades and stage of gliomas).
Kaplan–Meier plots were used for the determining the survival
difference of variables. Cox regression was used for identifying risk
factors of prognosis and included univariate and multivariate analyses
Clinically important variables that showed significance
(P<0.1) in the univariate analysis formed the input for
multivariate analysis using the Cox risk regression model with backward
elimination. Variables that showed significance (P<0.0001) in
the multivariate analysis were selected for developing a nomogram. CSS
was determined for 1, 3 and 5-years. Accuracy of the nomogram was
assessed using C-index and calibration curves generated following 1000
bootstrap resampling. A decision curve analysis is a net benefit
analysis that compares the true-positive to the weighted false-positive
rates across different risk thresholds that a clinician/patient might
want to accept. Based on the median value of the total scores in the
nomogram, a risk stratification model was built and comprised patients
who were divided into two prognostic groups. All statistical analyses
were performed using R and Empower Stats [www.empowerstats.com, XY
Solutions, Inc. Boston MA]. A two-tailed P value <0.05 was
considered statistically significant.