For the data that were not normally distributed (by Shapiro–Wilk normality test, p = 8.81 × 10−4), and the variances of which were heterogeneity (by Bartlett test of homogeneity of variances, p = 5.49 × 10−5), we used the Kruskal–Wallis H test to determine the statistical differences for multiple comparisons among more than two groups, followed by the Nemenyi test for two-group comparisons. The analysis was performed using the “pgirmess” package from R software (4.0.2). The survival analysis (accumulate survival probability) of different comparing groups was performed with the Kaplan–Meier procedure [38 ,39 ]. This procedure is a method to estimate the time-to-event method in the presence of censored cases. Within the Kaplan–Meier procedure, the equality of survival function was compared with the log-rank test [40 (link)] using the “survival” and “survminer” packages from R software (4.0.2), and the relating plots were generated by the “ggfortify” and “ggplot2” packages from R software (4.0.2).
Survminer
Manufactured by R Project for Statistical Computing
Sourced in Austria
Survminer is an R package that provides functions for the creation of attractive and informative survival plots. It offers a wide range of customization options, allowing users to easily generate publication-quality survival analysis graphics.
Automatically generated - may contain errors
Lab products found in correlation
Survival, by R Project for Statistical Computing (3 mentions)
R version 3, by R Project for Statistical Computing (3 mentions)
Tidyverse, by R Project for Statistical Computing (2 mentions)
Ggplot2, by R Project for Statistical Computing (1 mentions)
R script, by R Project for Statistical Computing (1 mentions)
Spss version 23, by IBM (1 mentions)
Prism 8, by GraphPad (1 mentions)
Cmprsk, by R Project for Statistical Computing (1 mentions)
Spss 27, by IBM (1 mentions)
14 protocols using survminer
1
Statistical Analysis of Survival Data
2
Survival Analysis of Gene Expression
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KMSA was performed using customized R scripts (http://www.r-project.org/ ). Patients’ gene expression, clinical and scoring data was matched using the assigned patient barcodes. For each gene expression or score, patients were assigned to two groups; Upper Quartile (UQ) and Lower Quartile (LQ) based on whether their gene expression or score values were in the bottom 25% or the top 25% of gene expression or score. KMSA was performed with the R package survminer (survminer/index.html">https://cran.r-project.org/web/packages/survminer/index.html ) using default arguments and Rho = 0, and the R package survival (survival/index.html">https://cran.r-project.org/web/packages/survival/index.html ) using default settings to extract a log rank test p-value for overall survival of up to 1825 days (5 years). p-values of significant associations were −log10 transformed for visualization.
3
Biomarker Analysis of PD-L1 in Cancer
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All experiments repeated three times, and the mean value of each sample was reported. The difference in PD-L1 mRNA and sPD-L1 expression in different subgroups was calculated by using independent-samples t-test. The difference in tPD-L1 expression and bOR in different subgroups was calculated by using Pearson’s chi-square test or Fisher’s exact test. Univariate and multivariate analyses were performed to identify independent factors of efficacy and OS. Survival analyses were performed by the Kaplan-Meier method and the log-rank test. SPSS version 23.0 (IBM, Armonk, NY, USA) was used for performing these statistical analyses. The “survival” and “survminer” packages from R software (version 3.5.2) were used for calculating the best cutoff point of each biomarker, conducting statistical calculations, and drawing Kaplan–Meier curves. A two-sided P value < 0.05 was considered statistically significant.
4
Predictors of Acute Kidney Injury in COVID-19
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Baseline clinical variables were assessed for normality. Normally distributed variables were summarized using mean and standard deviation. Non-normally distributed continuous variables were expressed as median (interquartile range). Categorical variables were expressed as absolute (n) and percentage. The primary outcome was time for AKI development. Patients were followed up until AKI development, death or recovery from COVID-19. Death or recovery without AKI development was a censoring event and AKI development was a competing event. The association of predictors of UCSD-Mayo risk score with the primary outcome was tested by cox regression analysis. The UCSD-Mayo risk score was ascertained using a receiver-operating characteristic (ROC) curve analysis and calibration curve analysis. Nagelkerke R2 was also calculated. Statistical analysis was performed using Package pROC version 1.16.2; Package survival version 3.2–7; Package survminer version 0.4.8; Package rms version 6.0–1 (R version 4.0.2, R Project for Statistical Computing, Vienna, Austria).
5
Tumor Mutational Burden Analysis in ICI Therapy
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We collected and analyzed the TMB data from the MSKCC ICI therapy cohort and the CheckMate ICI therapy cohort. TMB data from the MSKCC ICI therapy cohort were generated from the Memorial Sloan Kettering Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). TMB data from the CheckMate ICI therapy cohort were calculated as the sum of all nonsynonymous mutations in a sample according to Braun et al. (21 (link)). TMB in the 2 cohorts was further stratified by the R package “survminer” (version 0.4.8; The R Foundation for Statistical Computing) as low-TMB and high-TMB.
6
Prognostic Necroptosis-related DEGs
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We intersected DEGs with necroptosis-related genes to obtain 13 necroptosis-related DEGs (NRDEGs). Their differential expressions were further validated in the TCGA database. The KM plots of 12 genes were obtained via survminer package of R for visualization (version 0.4.9; R Foundation for Statistical Computing, Vienna, Austria) and survival package of R for statistical analysis of survival data (version 3.2-10), and of which 5 genes (KRT7, KRT19, CXCL5, IGF2BP3, and PKM) have prognostic significance. LASSO regression model (R package “glmnet”, version 4.1-2) was then utilized to narrow down the candidate genes and to develop the prognostic model. The risk score was calculated using the following formula: risk score = expression of Gene 1 × β1 + expression of Gene 2 × β2 + … expression of Gene n × βn, where β represents the regression coefficient of the genes in the signature.
7
PD Subtype Differences in Clinical Milestones
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To determine PD subtype differences in clinical milestones (DBS, dementia, mortality), multivariate Cox proportional hazards regression models using the longitudinal follow‐up data were conducted, with censoring based on last date of contact. SURVIVAL and events were calculated as such for each milestone: for DBS, events were defined as date of DBS and SURVIVAL time was calculated as time since baseline visit to most recent contact; for dementia, events were defined as the date when a participant received a CDR score ≥1, and SURVIVAL time was calculated as time since baseline visit to most recent CDR; and for mortality, events were defined as date of death, and SURVIVAL time was calculated as time since baseline to most recent contact. As follow‐up visits may only occur every three years, date of last contact (for the DBS and mortality analyses) was based on most recent contact from either a study visit, study contact, or clinical visit.
LCA was conducted using MPlus (Muthen & Muthen, Los Angeles CA). The longitudinal SURVIVAL analyses (Cox proportional hazards regression) were conducted in R Version 3.5.2, SURVIVAL and SURVMINER packages (R Foundation, Vienna Austria). Additional analyses were conducted with PASW Version 25 (IBM, Chicago, IL). All tests were 2‐tailed and P < 0.05 defined statistical significance.
LCA was conducted using MPlus (Muthen & Muthen, Los Angeles CA). The longitudinal SURVIVAL analyses (Cox proportional hazards regression) were conducted in R Version 3.5.2, SURVIVAL and SURVMINER packages (R Foundation, Vienna Austria). Additional analyses were conducted with PASW Version 25 (IBM, Chicago, IL). All tests were 2‐tailed and P < 0.05 defined statistical significance.
8
Survival Analysis Using R Packages
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We used Survival and Survminer packages (The R Foundation for Statistical Computing, Vienna, Austria) in R to analyze patient survival and prognosis. In total, 371 and 433 samples from the two databases were included, respectively. The patient follow-up time in the GEO database was 13 years, whereas that in TGGA database was 10 years. The survival curve was plotted using the Kaplan–Meier method, and the log-rank test was used to assess statistical significance. P <0.05 was a statistically significant standard.
9
Perioperative Dexamethasone Impact on Meningioma Outcomes
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Data were organized and analyzed using SPSS for Windows (version 29.0; IBM Corp., Armonk, NY, USA). Fisher´s exact test (two-sided) was performed to compare nominal variables and the student´s t-test for metric variables between patients with and without perioperative dexamethasone use. Only two-sided p-values are reported. Violin-Plots were created using GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA) to visualize the change of KPS and peritumoral T2-/FLAIR hyperintensities in those with or without perioperative dexamethasone intake. Receiver-operating characteristics (ROC) curve analysis was performed to identify the optimum cut-off value of the MIB-1 index in predicting the course of peritumoral T2-/FLAIR hyperintensities. ROC curve was created using ggplot2 in R. Multivariable binary logistic regression analyses were performed to analyze factors being associated with change in KPS from baseline to 3-months after surgery and development of peritumoral T2-/FLAIR hyperintensities. Sankey plots were created with the online RAWGraphs and modified with BioRender (27 ). We performed log-rank tests and created Kaplan-Meier charts of PFS in WHO grade 1 meningiomas who underwent Simpson grade I or II resections using the R package Survminer and Survival in R software version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).
10
Cervical Cancer TCGA RNA-Seq Analysis
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The Cancer Genome Atlas (TCGA) RNA-Sequencing (RNA-Seq) and associated clinical data for the cervical cancer cohort were retrieved from cBioPortal (https://www.cbioportal.org/ ). We included only the patients treated with CRT in our analysis, resulting in several n = 90 patients with Nek1 expression levels and clinical data available regarding disease-free survival (DFS). The cut-off for Nek1 expression levels (185.44 counts) was calculated using the maximally selected rank statistics that calculates the most optimal cut-off for continuous variables using log-rank statistics. This analysis was done using the R package “survminer” (Version 0.4.6, R foundation, Vienna, Austria).
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