Variables are summarized as means±standard deviations or counts (percentages). Statistical significance was set at p<0.05. The relationship between eGFR and ASAS Intake Score was confirmed using Pearson correlation. The correlation between the NSAID Intake Score and eGFR for each period (6 months, 1 year, 2 years, 3 years, 5 years, and 10 years) was calculated. As a subgroup analysis, the relationship between eGFR and ASAS Intake Score was investigated by dividing patients into three sections by their age: patients aged 18~40, 40~60, and 60 years or older (18~40, 40~60, 60~80 yr). R statistical language (version 3.6.1; Vienna, Austria; https://www.R-project.org/ ) was used for statistical analysis.
R statistical language version 3.6.1
Manufactured by R Project for Statistical Computing
R is an open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R version 3.6.1 is the latest stable release.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using r statistical language version 3.6.1
1
Relationship between eGFR and NSAID Intake
2
Survival Analysis of Immune Cell Infiltration
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The analysis was carried out entirely through R statistical language version 3.6.1 (https://www.R-project.org ). All of the tests had two sides, and a level of P < 0.05 was accepted as statistically significant. The continuous variables following normal distribution were compared by independent t-test, while those in skewness were compared by Mann-Whitney U test. In the light of the Pearson correlation coefficient, correlation matrices were schemed using R-software. We study the connection between OS and immune cell infiltration on the basis of the Kaplan-Meier curve, assessed with a log-rank test. The relevance between OS and immune cell infiltration was visualized by the K-M curve and further evaluated by log-rank test. Sensitivity and specificity in the predictive model of recurrence were analyzed by time-dependent ROC curves. The univariate regression model was applied in analyzing the influence of single-variable on survival. The LASSO-Cox regression models were used to identify the independent factors for survival. According to the Cox analysis, we used regression coefficients to build a nomogram.
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