98 lines
3.0 KiB
R
98 lines
3.0 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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source("utils.R")
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sma_init()
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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 = sma_break.time.by,
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surv.median.line = "hv",
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risk.table = "abs_pct",
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ggtheme = theme_bw())
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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 = sma_break.time.by,
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surv.median.line = "hv",
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risk.table = "abs_pct",
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ggtheme = theme_bw(),
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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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break.time.by = sma_break.time.by,
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surv.median.line = "hv",
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risk.table = "abs_pct",
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legend = "none",
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xlab = "Years",
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ggtheme = theme_bw())
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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 = sma_break.time.by,
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surv.median.line = "hv",
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risk.table = "abs_pct",
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ggtheme = theme_bw(),
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pval = TRUE)
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}
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# plot survival after diagnosis per diagnosis
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sma_plot_surv_dx_dx <- function() {
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ggsurvplot(survfit(surv_dx ~ diagnosis, data = secmal), data = secmal,
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xscale = "d_y", title = "Survival after diagnosis",
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break.time.by = sma_break.time.by,
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risk.table = "abs_pct",
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ggtheme = theme_bw())
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}
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# plot survival after diagnosis per diagnosis
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sma_plot_surv_asct_dx <- function() {
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ggsurvplot(survfit(surv_asct ~ diagnosis, data = secmal), data = secmal,
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xscale = "d_y", title = "Survival after transplantation",
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break.time.by = sma_break.time.by,
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risk.table = "abs_pct",
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ggtheme = theme_bw())
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}
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# plot survival after SM diagnosis
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sma_plot_surv_sm <- function() {
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ggsurvplot(survfit(Surv(event_time_sm, event_status) ~ 1, secmal),
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data = secmal, xscale = "d_y",
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break.time.by = 365.25,
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legend = "none",
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xlab = "Years",
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ggtheme = theme_bw())
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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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sma_plot_file("survival_dx_dx.png", png, sma_plot_surv_dx_dx)
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sma_plot_file("survival_asct_dx.png", png, sma_plot_surv_asct_dx)
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sma_plot_file("survival_sm.png", png, sma_plot_surv_sm)
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}
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