Separate 2 groups in Cox regression
2023-05-04
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 4.3.0 RC (2023-04-18 r84287 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## 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] VennDiagram_1.7.3 reshape2_1.4.4
## [3] forcats_1.0.0 glmSparseNet_1.19.0
## [5] glmnet_4.1-7 Matrix_1.5-4
## [7] TCGAutils_1.21.1 curatedTCGAData_1.23.2
## [9] MultiAssayExperiment_1.27.0 SummarizedExperiment_1.31.1
## [11] Biobase_2.61.0 GenomicRanges_1.53.1
## [13] GenomeInfoDb_1.37.1 IRanges_2.35.1
## [15] S4Vectors_0.39.1 BiocGenerics_0.47.0
## [17] MatrixGenerics_1.13.0 matrixStats_0.63.0
## [19] futile.logger_1.4.3 survival_3.5-5
## [21] ggplot2_3.4.2 dplyr_1.1.2
## [23] BiocStyle_2.29.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_1.8.4 shape_1.4.6
## [3] magrittr_2.0.3 magick_2.7.4
## [5] GenomicFeatures_1.53.0 farver_2.1.1
## [7] rmarkdown_2.21 BiocIO_1.11.0
## [9] zlibbioc_1.47.0 vctrs_0.6.2
## [11] memoise_2.0.1 Rsamtools_2.17.0
## [13] RCurl_1.98-1.12 rstatix_0.7.2
## [15] htmltools_0.5.5 S4Arrays_1.1.2
## [17] BiocBaseUtils_1.3.0 progress_1.2.2
## [19] AnnotationHub_3.9.1 lambda.r_1.2.4
## [21] curl_5.0.0 broom_1.0.4
## [23] pROC_1.18.0 SparseArray_1.1.2
## [25] sass_0.4.6 bslib_0.4.2
## [27] plyr_1.8.8 zoo_1.8-12
## [29] futile.options_1.0.1 cachem_1.0.8
## [31] GenomicAlignments_1.37.0 mime_0.12
## [33] lifecycle_1.0.3 iterators_1.0.14
## [35] pkgconfig_2.0.3 R6_2.5.1
## [37] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [39] shiny_1.7.4 digest_0.6.31
## [41] colorspace_2.1-0 AnnotationDbi_1.63.1
## [43] ExperimentHub_2.9.0 RSQLite_2.3.1
## [45] ggpubr_0.6.0 filelock_1.0.2
## [47] labeling_0.4.2 km.ci_0.5-6
## [49] fansi_1.0.4 abind_1.4-5
## [51] httr_1.4.5 compiler_4.3.0
## [53] bit64_4.0.5 withr_2.5.0
## [55] backports_1.4.1 BiocParallel_1.35.0
## [57] carData_3.0-5 DBI_1.1.3
## [59] highr_0.10 ggsignif_0.6.4
## [61] biomaRt_2.57.0 rappdirs_0.3.3
## [63] DelayedArray_0.27.2 rjson_0.2.21
## [65] tools_4.3.0 interactiveDisplayBase_1.39.0
## [67] httpuv_1.6.9 glue_1.6.2
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## [95] knitr_1.42 gridExtra_2.3
## [97] bookdown_0.33 xfun_0.39
## [99] stringi_1.7.12 yaml_2.3.7
## [101] evaluate_0.20 codetools_0.2-19
## [103] tibble_3.2.1 BiocManager_1.30.20
## [105] cli_3.6.1 xtable_1.8-4
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## [125] rvest_1.0.3 formatR_1.14