Separate 2 groups in Cox regression
2022-05-15
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
.Last.value <- flog.layout(layout.format('[~l] ~m'))
.Last.value <- 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)
res.age <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)1.2.1 Kaplan-Meier survival results
## Call: survfit(formula = survival::Surv(time, status) ~ group, data = prognostic.index.df)
##
## n events median 0.95LCL 0.95UCL
## Low risk 13 4 NA 638 NA
## High risk 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%)
res.age.40.60 <-
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 = prognostic.index.df)
##
## n events median 0.95LCL 0.95UCL
## Low risk 11 3 NA 563 NA
## High risk 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.
res.age.60.40 <- separate2GroupsCox(
chosen.btas = c(age = 1, 0),
xdata,
ydata,
probs = c(.6, .4),
stop.when.overlap = FALSE
)## Warning in separate2GroupsCox(chosen.btas = c(age = 1, 0), xdata, ydata, : 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 stop.when.overlap 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 = prognostic.index.df)
##
## n events median 0.95LCL 0.95UCL
## Low risk 16 5 NA 638 NA
## High risk 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 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] VennDiagram_1.7.3 reshape2_1.4.4
## [3] forcats_0.5.1 glmSparseNet_1.14.1
## [5] glmnet_4.1-4 Matrix_1.4-1
## [7] TCGAutils_1.16.0 curatedTCGAData_1.18.0
## [9] MultiAssayExperiment_1.22.0 SummarizedExperiment_1.26.1
## [11] Biobase_2.56.0 GenomicRanges_1.48.0
## [13] GenomeInfoDb_1.32.2 IRanges_2.30.0
## [15] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [17] MatrixGenerics_1.8.0 matrixStats_0.62.0
## [19] futile.logger_1.4.3 survival_3.3-1
## [21] ggplot2_3.3.6 dplyr_1.0.9
## [23] BiocStyle_2.24.0
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