Data were summarized by frequency for categorical variables and by median with interquartile range (IQR) for continuous variables. TCR was dichotomized by calculating the area under the ROC curve using OS as the gold standard to identify the optimal cutoff time. Categorical data were compared using the chi-square test. Survival rates were estimated using the Kaplan–Meier method and compared with the log-rank test. Multivariate analysis was performed using Cox proportional hazards regression. A P value <0.05 was considered statistically significant. Statistical analyses were performed with SPSS 23.0 software (IBM Corp., NY, USA) and the R statistical software (version 3.6.2).
R statistical software version 3.6.2
Manufactured by IBM
Sourced in United States
R is an open-source software environment for statistical computing and graphics. Version 3.6.2 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using r statistical software version 3.6.2
1
Optimal TCR Cutoff for Survival Prediction
2
Stroke Prediction by Rheumatoid Arthritis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Categorical variables were expressed as n (%) and continuous variables as means (standard deviation) or medians (interquartile range). Differences in baseline characteristics between groups were analyzed using chi-square test or Fisher’s exact test, analysis of variance, or Kruskal–Wallis test where appropriate. Logistic regression analysis was used to estimate the risk of stroke outcomes by calculating odds ratios (OR) and 95% confidence interval (CI). Variables with P value < 0.1 in univariate analysis were adjusted in multiple regression analysis. In addition, net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the predictive value of adding RA to conventional risk factors model. Receiver operating characteristic (ROC) curves were used to describe RA levels as a potential predictive factor for stroke outcomes. The area under curve (AUC) was calculated based on the ROC curves. Two-tailed P < 0.05 was considered to be statistically significant. All analyses were performed using SPSS software, version 22.0 (IBM, New York, NY) and R statistical software version 3.6.2.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!