Jens Sauer
12ffcd4e38
This will create the plot using the ggsurvplot from "survminer" package. survminer user ggplot for plotting and comes with some powerful and nice looking features.
57 lines
1.7 KiB
R
57 lines
1.7 KiB
R
# secMalASCT survival calculation
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#
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# License: GPL version 3
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# Jens Mathis Sauer (c) 2020
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library(survival)
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library(survminer)
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source("utils.R")
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secmal <- sma_load_data("current.csv")
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# Setup survival object
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surv_dx <- Surv(time = secmal$event_time_dx, event = secmal$event_status)
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surv_asct <- Surv(time = secmal$event_time_asct, event = secmal$event_status)
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# plot survival after diagnosis
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sma_plot_surv_dx <- function() {
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ggsurvplot(survfit(surv_dx ~ 1), data = secmal, xscale = "d_y",
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title = "Survival after diagnosis",
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break.time.by = 5 * 365.25)
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}
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# plot survival after diagnosis per sex
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sma_plot_surv_dx_sex <- function() {
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ggsurvplot(survfit(surv_dx ~ sex, data = secmal), data = secmal,
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xscale = "d_y", title = "Survival after diagnosis",
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legend.labs = c("Female", "Male"),
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break.time.by = 5 * 365.25,
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pval = TRUE)
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}
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# plot survival after transplantation
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sma_plot_surv_asct <- function() {
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ggsurvplot(survfit(surv_asct ~ 1), data = secmal, xscale = "d_y",
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title = "Survival after transplantation",
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break.time.by = 5 * 365.25)
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}
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# plot survival after transplantation per sex
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sma_plot_surv_asct_sex <- function() {
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ggsurvplot(survfit(surv_asct ~ sex, data = secmal), data = secmal,
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xscale = "d_y", title = "Survival after transplantation",
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legend.labs = c("Female", "Male"),
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break.time.by = 5 * 365.25,
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pval = TRUE)
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}
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#
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# Write survival plots to files
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#
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sma_plot_file_surv <- function() {
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sma_plot_file("survival_dx.png", png, sma_plot_surv_dx)
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sma_plot_file("survival_dx_sex.png", png, sma_plot_surv_dx_sex)
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sma_plot_file("survival_asct.png", png, sma_plot_surv_asct)
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sma_plot_file("survival_asct_sex.png", png, sma_plot_surv_asct_sex)
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}
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