Separate 2 groups in Cox regression
2026-05-18
Contents
0.1 Instalation
if (!require("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("glmSparseNet")1 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())1.1 Prepare data
data("cancer", package = "survival")
xdata <- survival::ovarian[, c("age", "resid.ds")]
ydata <- data.frame(
time = survival::ovarian$futime,
status = survival::ovarian$fustat
)1.2 Separate using age as co-variate
(group cutoff is median calculated relative risk)
resAge <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)1.2.1 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 NA1.2.2 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.
1.3 Separate using age as co-variate (group cutoff is 40% - 60%)
resAge4060 <-
separate2GroupsCox(c(age = 1, 0),
xdata,
ydata,
probs = c(.4, .6)
)1.3.1 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 NA1.3.2 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.
1.4 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 xdata1.4.1 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 NA1.4.2 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.
2 Session Info
sessionInfo()## R version 4.6.0 Patched (2026-05-01 r89994)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] glmnet_5.0 VennDiagram_1.8.2
## [3] reshape2_1.4.5 forcats_1.0.1
## [5] Matrix_1.7-5 glmSparseNet_1.31.0
## [7] TCGAutils_1.33.1 curatedTCGAData_1.35.0
## [9] MultiAssayExperiment_1.39.0 SummarizedExperiment_1.43.0
## [11] Biobase_2.73.1 GenomicRanges_1.65.0
## [13] Seqinfo_1.3.0 IRanges_2.47.1
## [15] S4Vectors_0.51.2 BiocGenerics_0.59.2
## [17] generics_0.1.4 MatrixGenerics_1.25.0
## [19] matrixStats_1.5.0 futile.logger_1.4.9
## [21] survival_3.8-6 ggplot2_4.0.3
## [23] dplyr_1.2.1 BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_2.0.0
## [3] shape_1.4.6.1 magrittr_2.0.5
## [5] magick_2.9.1 GenomicFeatures_1.65.0
## [7] farver_2.1.2 rmarkdown_2.31
## [9] BiocIO_1.23.3 vctrs_0.7.3
## [11] memoise_2.0.1 Rsamtools_2.29.0
## [13] RCurl_1.98-1.18 rstatix_0.7.3
## [15] tinytex_0.59 htmltools_0.5.9
## [17] S4Arrays_1.13.0 BiocBaseUtils_1.15.1
## [19] progress_1.2.3 AnnotationHub_4.3.0
## [21] lambda.r_1.2.4 curl_7.1.0
## [23] broom_1.0.13 Formula_1.2-5
## [25] pROC_1.19.0.1 SparseArray_1.13.2
## [27] sass_0.4.10 bslib_0.11.0
## [29] plyr_1.8.9 httr2_1.2.2
## [31] futile.options_1.0.1 cachem_1.1.0
## [33] GenomicAlignments_1.49.0 lifecycle_1.0.5
## [35] iterators_1.0.14 pkgconfig_2.0.3
## [37] R6_2.6.1 fastmap_1.2.0
## [39] digest_0.6.39 AnnotationDbi_1.75.0
## [41] ExperimentHub_3.3.0 RSQLite_3.52.0
## [43] ggpubr_0.6.3 labeling_0.4.3
## [45] filelock_1.0.3 httr_1.4.8
## [47] abind_1.4-8 compiler_4.6.0
## [49] bit64_4.8.0 withr_3.0.2
## [51] S7_0.2.2 backports_1.5.1
## [53] BiocParallel_1.47.0 carData_3.0-6
## [55] DBI_1.3.0 ggsignif_0.6.4
## [57] biomaRt_2.69.0 rappdirs_0.3.4
## [59] DelayedArray_0.39.2 rjson_0.2.23
## [61] tools_4.6.0 chromote_0.5.1
## [63] otel_0.2.0 glue_1.8.1
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## [67] checkmate_2.3.4 gtable_0.3.6
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## [95] yaml_2.3.12 evaluate_1.0.5
## [97] codetools_0.2-20 cigarillo_1.3.0
## [99] tibble_3.3.1 BiocManager_1.30.27
## [101] cli_3.6.6 processx_3.9.0
## [103] jquerylib_0.1.4 dichromat_2.0-0.1
## [105] Rcpp_1.1.1-1.1 GenomeInfoDb_1.49.0
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