Separate 2 groups in Cox regression
2025-04-15
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 4.5.0 beta (2025-04-02 r88102)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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: America/New_York
## 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_1.7-3 glmSparseNet_1.27.0
## [7] TCGAutils_1.29.0 curatedTCGAData_1.29.2
## [9] MultiAssayExperiment_1.35.0 SummarizedExperiment_1.39.0
## [11] Biobase_2.69.0 GenomicRanges_1.61.0
## [13] GenomeInfoDb_1.45.0 IRanges_2.43.0
## [15] S4Vectors_0.47.0 BiocGenerics_0.55.0
## [17] generics_0.1.3 MatrixGenerics_1.21.0
## [19] matrixStats_1.5.0 futile.logger_1.4.3
## [21] survival_3.8-3 ggplot2_3.5.2
## [23] dplyr_1.1.4 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_2.0.0 shape_1.4.6.1
## [3] magrittr_2.0.3 magick_2.8.6
## [5] GenomicFeatures_1.61.0 farver_2.1.2
## [7] rmarkdown_2.29 BiocIO_1.19.0
## [9] vctrs_0.6.5 memoise_2.0.1
## [11] Rsamtools_2.25.0 RCurl_1.98-1.17
## [13] rstatix_0.7.2 tinytex_0.57
## [15] htmltools_0.5.8.1 S4Arrays_1.9.0
## [17] BiocBaseUtils_1.11.0 progress_1.2.3
## [19] AnnotationHub_3.17.0 lambda.r_1.2.4
## [21] curl_6.2.2 broom_1.0.8
## [23] Formula_1.2-5 pROC_1.18.5
## [25] SparseArray_1.9.0 sass_0.4.10
## [27] bslib_0.9.0 plyr_1.8.9
## [29] httr2_1.1.2 zoo_1.8-14
## [31] futile.options_1.0.1 cachem_1.1.0
## [33] GenomicAlignments_1.45.0 mime_0.13
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## [43] AnnotationDbi_1.71.0 ps_1.9.1
## [45] ExperimentHub_2.17.0 RSQLite_2.3.9
## [47] ggpubr_0.6.0 labeling_0.4.3
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## [51] httr_1.4.7 abind_1.4-8
## [53] compiler_4.5.0 bit64_4.6.0-1
## [55] withr_3.0.2 backports_1.5.0
## [57] BiocParallel_1.43.0 carData_3.0-5
## [59] DBI_1.2.3 ggsignif_0.6.4
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## [79] car_3.1-3 xml2_1.3.8
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## [91] bit_4.6.0 tidyselect_1.2.1
## [93] Biostrings_2.77.0 knitr_1.50
## [95] gridExtra_2.3 bookdown_0.43
## [97] xfun_0.52 stringi_1.8.7
## [99] UCSC.utils_1.5.0 yaml_2.3.10
## [101] evaluate_1.0.3 codetools_0.2-20
## [103] tibble_3.2.1 BiocManager_1.30.25
## [105] cli_3.6.4 xtable_1.8-4
## [107] munsell_0.5.1 processx_3.8.6
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## [125] rvest_1.0.4 formatR_1.14