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 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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_4.1-8                VennDiagram_1.7.3          
##  [3] reshape2_1.4.4              forcats_1.0.0              
##  [5] Matrix_1.7-2                glmSparseNet_1.25.0        
##  [7] TCGAutils_1.27.6            curatedTCGAData_1.28.1     
##  [9] MultiAssayExperiment_1.33.8 SummarizedExperiment_1.37.0
## [11] Biobase_2.67.0              GenomicRanges_1.59.1       
## [13] GenomeInfoDb_1.43.4         IRanges_2.41.2             
## [15] S4Vectors_0.45.2            BiocGenerics_0.53.5        
## [17] generics_0.1.3              MatrixGenerics_1.19.1      
## [19] matrixStats_1.5.0           futile.logger_1.4.3        
## [21] survival_3.8-3              ggplot2_3.5.1              
## [23] dplyr_1.1.4                 BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##   [1] sys_3.4.3                 jsonlite_1.8.9           
##   [3] shape_1.4.6.1             magrittr_2.0.3           
##   [5] GenomicFeatures_1.59.1    farver_2.1.2             
##   [7] rmarkdown_2.29            BiocIO_1.17.1            
##   [9] vctrs_0.6.5               memoise_2.0.1            
##  [11] Rsamtools_2.23.1          RCurl_1.98-1.16          
##  [13] rstatix_0.7.2             htmltools_0.5.8.1        
##  [15] S4Arrays_1.7.1            BiocBaseUtils_1.9.0      
##  [17] progress_1.2.3            AnnotationHub_3.15.0     
##  [19] lambda.r_1.2.4            curl_6.2.0               
##  [21] broom_1.0.7               Formula_1.2-5            
##  [23] SparseArray_1.7.4         pROC_1.18.5              
##  [25] sass_0.4.9                bslib_0.8.0              
##  [27] plyr_1.8.9                httr2_1.1.0              
##  [29] zoo_1.8-12                futile.options_1.0.1     
##  [31] cachem_1.1.0              buildtools_1.0.0         
##  [33] GenomicAlignments_1.43.0  mime_0.12                
##  [35] lifecycle_1.0.4           iterators_1.0.14         
##  [37] pkgconfig_2.0.3           R6_2.5.1                 
##  [39] fastmap_1.2.0             GenomeInfoDbData_1.2.13  
##  [41] digest_0.6.37             colorspace_2.1-1         
##  [43] AnnotationDbi_1.69.0      ExperimentHub_2.15.0     
##  [45] RSQLite_2.3.9             ggpubr_0.6.0             
##  [47] filelock_1.0.3            labeling_0.4.3           
##  [49] km.ci_0.5-6               httr_1.4.7               
##  [51] abind_1.4-8               compiler_4.4.2           
##  [53] bit64_4.6.0-1             withr_3.0.2              
##  [55] backports_1.5.0           BiocParallel_1.41.0      
##  [57] carData_3.0-5             DBI_1.2.3                
##  [59] ggsignif_0.6.4            biomaRt_2.63.0           
##  [61] rappdirs_0.3.3            DelayedArray_0.33.4      
##  [63] rjson_0.2.23              tools_4.4.2              
##  [65] glue_1.8.0                restfulr_0.0.15          
##  [67] checkmate_2.3.2           gtable_0.3.6             
##  [69] KMsurv_0.1-5              tzdb_0.4.0               
##  [71] tidyr_1.3.1               survminer_0.5.0          
##  [73] data.table_1.16.4         hms_1.1.3                
##  [75] car_3.1-3                 xml2_1.3.6               
##  [77] XVector_0.47.2            BiocVersion_3.21.1       
##  [79] foreach_1.5.2             pillar_1.10.1            
##  [81] stringr_1.5.1             splines_4.4.2            
##  [83] BiocFileCache_2.15.1      lattice_0.22-6           
##  [85] rtracklayer_1.67.0        bit_4.5.0.1              
##  [87] tidyselect_1.2.1          maketools_1.3.1          
##  [89] Biostrings_2.75.3         knitr_1.49               
##  [91] gridExtra_2.3             xfun_0.50                
##  [93] stringi_1.8.4             UCSC.utils_1.3.1         
##  [95] yaml_2.3.10               evaluate_1.0.3           
##  [97] codetools_0.2-20          tibble_3.2.1             
##  [99] BiocManager_1.30.25       cli_3.6.3                
## [101] xtable_1.8-4              munsell_0.5.1            
## [103] jquerylib_0.1.4           survMisc_0.5.6           
## [105] Rcpp_1.0.14               GenomicDataCommons_1.31.0
## [107] dbplyr_2.5.0              png_0.1-8                
## [109] XML_3.99-0.18             readr_2.1.5              
## [111] blob_1.2.4                prettyunits_1.2.0        
## [113] bitops_1.0-9              scales_1.3.0             
## [115] purrr_1.0.2               crayon_1.5.3             
## [117] rlang_1.1.5               KEGGREST_1.47.0          
## [119] rvest_1.0.4               formatR_1.14