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 4.4.1 (2024-06-14)
## 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_1.7-0                glmSparseNet_1.23.0        
##  [7] TCGAutils_1.25.1            curatedTCGAData_1.27.0     
##  [9] MultiAssayExperiment_1.31.5 SummarizedExperiment_1.35.1
## [11] Biobase_2.65.1              GenomicRanges_1.57.1       
## [13] GenomeInfoDb_1.41.1         IRanges_2.39.2             
## [15] S4Vectors_0.43.2            BiocGenerics_0.51.2        
## [17] MatrixGenerics_1.17.0       matrixStats_1.4.1          
## [19] futile.logger_1.4.3         survival_3.7-0             
## [21] ggplot2_3.5.1               dplyr_1.1.4                
## [23] BiocStyle_2.33.1           
## 
## loaded via a namespace (and not attached):
##   [1] sys_3.4.2                 jsonlite_1.8.9           
##   [3] shape_1.4.6.1             magrittr_2.0.3           
##   [5] GenomicFeatures_1.57.0    farver_2.1.2             
##   [7] rmarkdown_2.28            BiocIO_1.15.2            
##   [9] zlibbioc_1.51.1           vctrs_0.6.5              
##  [11] memoise_2.0.1             Rsamtools_2.21.2         
##  [13] RCurl_1.98-1.16           rstatix_0.7.2            
##  [15] htmltools_0.5.8.1         S4Arrays_1.5.9           
##  [17] BiocBaseUtils_1.7.3       progress_1.2.3           
##  [19] AnnotationHub_3.13.3      lambda.r_1.2.4           
##  [21] curl_5.2.3                broom_1.0.7              
##  [23] pROC_1.18.5               SparseArray_1.5.40       
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##  [37] pkgconfig_2.0.3           R6_2.5.1                 
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##  [41] digest_0.6.37             colorspace_2.1-1         
##  [43] AnnotationDbi_1.67.0      ExperimentHub_2.13.1     
##  [45] RSQLite_2.3.7             ggpubr_0.6.0             
##  [47] filelock_1.0.3            labeling_0.4.3           
##  [49] km.ci_0.5-6               fansi_1.0.6              
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##  [53] compiler_4.4.1            bit64_4.5.2              
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##  [61] ggsignif_0.6.4            biomaRt_2.61.3           
##  [63] rappdirs_0.3.3            DelayedArray_0.31.11     
##  [65] rjson_0.2.23              tools_4.4.1              
##  [67] glue_1.7.0                restfulr_0.0.15          
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##  [77] hms_1.1.3                 car_3.1-2                
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##  [81] XVector_0.45.0            BiocVersion_3.20.0       
##  [83] foreach_1.5.2             pillar_1.9.0             
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##  [87] BiocFileCache_2.13.0      lattice_0.22-6           
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##  [91] tidyselect_1.2.1          maketools_1.3.0          
##  [93] Biostrings_2.73.2         knitr_1.48               
##  [95] gridExtra_2.3             xfun_0.47                
##  [97] stringi_1.8.4             UCSC.utils_1.1.0         
##  [99] yaml_2.3.10               evaluate_1.0.0           
## [101] codetools_0.2-20          tibble_3.2.1             
## [103] BiocManager_1.30.25       cli_3.6.3                
## [105] xtable_1.8-4              munsell_0.5.1            
## [107] jquerylib_0.1.4           survMisc_0.5.6           
## [109] Rcpp_1.0.13               GenomicDataCommons_1.29.6
## [111] dbplyr_2.5.0              png_0.1-8                
## [113] XML_3.99-0.17             readr_2.1.5              
## [115] blob_1.2.4                prettyunits_1.2.0        
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## [121] rlang_1.1.4               KEGGREST_1.45.1          
## [123] rvest_1.0.4               formatR_1.14