Separate 2 groups in Cox regression
2024-10-29
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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.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_1.7-1 glmSparseNet_1.24.0
## [7] TCGAutils_1.26.0 curatedTCGAData_1.27.1
## [9] MultiAssayExperiment_1.32.0 SummarizedExperiment_1.36.0
## [11] Biobase_2.66.0 GenomicRanges_1.58.0
## [13] GenomeInfoDb_1.42.0 IRanges_2.40.0
## [15] S4Vectors_0.44.0 BiocGenerics_0.52.0
## [17] MatrixGenerics_1.18.0 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_2.34.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.9 shape_1.4.6.1
## [3] magrittr_2.0.3 magick_2.8.5
## [5] GenomicFeatures_1.58.0 farver_2.1.2
## [7] rmarkdown_2.28 BiocIO_1.16.0
## [9] zlibbioc_1.52.0 vctrs_0.6.5
## [11] memoise_2.0.1 Rsamtools_2.22.0
## [13] RCurl_1.98-1.16 rstatix_0.7.2
## [15] tinytex_0.53 progress_1.2.3
## [17] htmltools_0.5.8.1 S4Arrays_1.6.0
## [19] BiocBaseUtils_1.8.0 AnnotationHub_3.14.0
## [21] lambda.r_1.2.4 curl_5.2.3
## [23] broom_1.0.7 Formula_1.2-5
## [25] pROC_1.18.5 SparseArray_1.6.0
## [27] sass_0.4.9 bslib_0.8.0
## [29] plyr_1.8.9 httr2_1.0.5
## [31] zoo_1.8-12 futile.options_1.0.1
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## [53] httr_1.4.7 abind_1.4-8
## [55] compiler_4.4.1 bit64_4.5.2
## [57] withr_3.0.2 backports_1.5.0
## [59] BiocParallel_1.40.0 carData_3.0-5
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## [99] knitr_1.48 gridExtra_2.3
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## [105] yaml_2.3.10 evaluate_1.0.1
## [107] codetools_0.2-20 tibble_3.2.1
## [109] BiocManager_1.30.25 cli_3.6.3
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