Separate 2 groups in Cox regression

Instalation

if (!require("BiocManager")) {
    install.packages("BiocManager")
}
BiocManager::install("glmSparseNet")

Required Packages

library(futile.logger)
library(ggplot2)
library(glmSparseNet)
library(survival)

# Some general options for futile.logger the debugging package
flog.layout(layout.format("[~l] ~m"))
options("glmSparseNet.show_message" = FALSE)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())

Prepare data

data("cancer", package = "survival")
xdata <- survival::ovarian[, c("age", "resid.ds")]
ydata <- data.frame(
    time = survival::ovarian$futime,
    status = survival::ovarian$fustat
)

Separate using age as co-variate

(group cutoff is median calculated relative risk)

resAge <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)

Kaplan-Meier survival results

## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  13      4     NA     638      NA
## High risk - 1 13      8    464     268      NA

Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below or equal the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

Separate using age as co-variate (group cutoff is 40% - 60%)

resAge4060 <-
    separate2GroupsCox(c(age = 1, 0),
        xdata,
        ydata,
        probs = c(.4, .6)
    )

Kaplan-Meier survival results

## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  11      3     NA     563      NA
## High risk - 1 10      7    359     156      NA

Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

Separate using age as co-variate (group cutoff is 60% - 40%)

This is a special case where you want to use a cutoff that includes some sample on both high and low risks groups.

resAge6040 <- separate2GroupsCox(
    chosenBetas = c(age = 1, 0),
    xdata,
    ydata,
    probs = c(.6, .4),
    stopWhenOverlap = FALSE
)
## Warning in buildPrognosticIndexDataFrame(ydata, probs, stopWhenOverlap, : The cutoff values given to the function allow for some over samples in both groups, with:
##   high risk size (15) + low risk size (16) not equal to xdata/ydata rows (31 != 26)
## 
## We are continuing with execution as parameter `stopWhenOverlap` is FALSE.
##   note: This adds duplicate samples to ydata and xdata xdata

Kaplan-Meier survival results

## Kaplan-Meier results
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognosticIndexDf)
## 
##                n events median 0.95LCL 0.95UCL
## Low risk - 1  16      5     NA     638      NA
## High risk - 1 15      9    475     353      NA

Plot

A individual is attributed to low-risk group if its calculated relative risk (using Cox Proportional model) is below the median risk.

The opposite for the high-risk groups, populated with individuals above the median relative-risk.

Session Info

sessionInfo()
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] glmnet_4.1-10               VennDiagram_1.7.3          
##  [3] reshape2_1.4.5              forcats_1.0.1              
##  [5] Matrix_1.7-4                glmSparseNet_1.28.0        
##  [7] TCGAutils_1.31.3            curatedTCGAData_1.32.0     
##  [9] MultiAssayExperiment_1.36.0 SummarizedExperiment_1.40.0
## [11] Biobase_2.70.0              GenomicRanges_1.62.0       
## [13] Seqinfo_1.0.0               IRanges_2.44.0             
## [15] S4Vectors_0.48.0            BiocGenerics_0.56.0        
## [17] generics_0.1.4              MatrixGenerics_1.22.0      
## [19] matrixStats_1.5.0           futile.logger_1.4.3        
## [21] survival_3.8-3              ggplot2_4.0.1              
## [23] dplyr_1.1.4                 BiocStyle_2.38.0           
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3        sys_3.4.3                
##   [3] jsonlite_2.0.0            shape_1.4.6.1            
##   [5] magrittr_2.0.4            GenomicFeatures_1.62.0   
##   [7] farver_2.1.2              rmarkdown_2.30           
##   [9] BiocIO_1.20.0             vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.26.0         
##  [13] RCurl_1.98-1.17           rstatix_0.7.3            
##  [15] htmltools_0.5.8.1         S4Arrays_1.10.0          
##  [17] BiocBaseUtils_1.12.0      progress_1.2.3           
##  [19] AnnotationHub_4.0.0       lambda.r_1.2.4           
##  [21] curl_7.0.0                broom_1.0.10             
##  [23] Formula_1.2-5             SparseArray_1.10.1       
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##  [41] AnnotationDbi_1.72.0      ExperimentHub_3.0.0      
##  [43] RSQLite_2.4.4             ggpubr_0.6.2             
##  [45] filelock_1.0.3            labeling_0.4.3           
##  [47] km.ci_0.5-6               httr_1.4.7               
##  [49] abind_1.4-8               compiler_4.5.2           
##  [51] bit64_4.6.0-1             withr_3.0.2              
##  [53] S7_0.2.1                  backports_1.5.0          
##  [55] BiocParallel_1.44.0       carData_3.0-5            
##  [57] DBI_1.2.3                 ggsignif_0.6.4           
##  [59] biomaRt_2.66.0            rappdirs_0.3.3           
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##  [63] tools_4.5.2               glue_1.8.0               
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##  [67] gtable_0.3.6              KMsurv_0.1-6             
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##  [71] survminer_0.5.1           data.table_1.17.8        
##  [73] hms_1.1.4                 car_3.1-3                
##  [75] xml2_1.4.1                XVector_0.50.0           
##  [77] BiocVersion_3.23.1        foreach_1.5.2            
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##  [91] xfun_0.54                 stringi_1.8.7            
##  [93] UCSC.utils_1.6.0          yaml_2.3.10              
##  [95] evaluate_1.0.5            codetools_0.2-20         
##  [97] cigarillo_1.0.0           tibble_3.3.0             
##  [99] BiocManager_1.30.27       cli_3.6.5                
## [101] xtable_1.8-4              jquerylib_0.1.4          
## [103] survMisc_0.5.6            Rcpp_1.1.0               
## [105] GenomeInfoDb_1.46.0       GenomicDataCommons_1.34.1
## [107] dbplyr_2.5.1              png_0.1-8                
## [109] XML_3.99-0.20             readr_2.1.6              
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## [119] rvest_1.0.5               formatR_1.14