# secMalASCT survival calculation
#
# License: GPL version 3
# Jens Mathis Sauer (c) 2020

source("utils.R")
sma_init()

# Setup survival object
surv_dx <- Surv(time = secmal$event_time_dx, event = secmal$event_status)
surv_asct <- Surv(time = secmal$event_time_asct, event = secmal$event_status)

# plot survival after diagnosis
sma_plot_surv_dx <- function() {
	ggsurvplot(survfit(surv_dx ~ 1), data = secmal, xscale = "d_y",
		   title = "Survival after diagnosis",
		   break.time.by = sma_break.time.by,
		   surv.median.line = "hv",
		   risk.table = "abs_pct",
		   ggtheme = theme_bw())
}

# plot survival after diagnosis per sex
sma_plot_surv_dx_sex <- function() {
	ggsurvplot(survfit(surv_dx ~ sex, data = secmal), data = secmal,
		   xscale = "d_y", title = "Survival after diagnosis",
		   legend.labs = c("Female", "Male"),
		   break.time.by = sma_break.time.by,
		   surv.median.line = "hv",
		   risk.table = "abs_pct",
		   ggtheme = theme_bw(),
		   pval = TRUE)
}

# plot survival after transplantation
sma_plot_surv_asct <- function() {
	ggsurvplot(survfit(surv_asct ~ 1), data = secmal, xscale = "d_y",
		   break.time.by = sma_break.time.by,
		   surv.median.line = "hv",
		   risk.table = "abs_pct",
		   legend = "none",
		   xlab = "Years",
		   palette = "lancet",
		   ggtheme = theme_bw())
}

# plot survival after transplantation per sex
sma_plot_surv_asct_sex <- function() {
	ggsurvplot(survfit(surv_asct ~ sex, data = secmal), data = secmal,
		   xscale = "d_y", title = "Survival after transplantation",
		   legend.labs = c("Female", "Male"),
		   break.time.by = sma_break.time.by,
		   surv.median.line = "hv",
		   risk.table = "abs_pct",
		   ggtheme = theme_bw(),
		   pval = TRUE)
}

# plot survival after diagnosis per diagnosis
sma_plot_surv_dx_dx <- function() {
	ggsurvplot(survfit(surv_dx ~ diagnosis, data = secmal), data = secmal,
		   xscale = "d_y", title = "Survival after diagnosis",
		   break.time.by = sma_break.time.by,
		   risk.table = "abs_pct",
		   ggtheme = theme_bw())
}

# plot survival after diagnosis per diagnosis
sma_plot_surv_asct_dx <- function() {
	ggsurvplot(survfit(surv_asct ~ diagnosis, data = secmal), data = secmal,
		   xscale = "d_y", title = "Survival after transplantation",
		   break.time.by = sma_break.time.by,
		   risk.table = "abs_pct",
		   ggtheme = theme_bw())
}

# plot survival after SM diagnosis
sma_plot_surv_sm <- function() {
	ggsurvplot(survfit(Surv(event_time_sm, event_status) ~ 1, secmal),
		   data = secmal, xscale = "d_y",
		   break.time.by = 365.25,
		   legend = "none",
		   xlab = "Years",
		   palette = "lancet",
		   ggtheme = theme_bw())
}

sma_plot_surv_sm_nonsm <- function() {
	ggsurvplot(survfit(Surv(event_time_asct, event_status) ~ ifelse(
		time_at_risk_status == 1, 1, 0), secmal),
		   data = secmal, xscale = "d_y",
		   break.time.by = sma_break.time.by,
		   legend = "none",
		   xlab = "Years",
		   palette = "lancet",
		   ggtheme = theme_bw(),
		   conf.int = TRUE,
		   pval = TRUE)
}


#
# Write survival plots to files
#
sma_plot_file_surv <- function() {
	sma_plot_file("survival_dx.png", png, sma_plot_surv_dx)
	sma_plot_file("survival_dx_sex.png", png, sma_plot_surv_dx_sex)
	sma_plot_file("survival_asct.png", png, sma_plot_surv_asct)
	sma_plot_file("survival_asct_sex.png", png, sma_plot_surv_asct_sex)
	sma_plot_file("survival_dx_dx.png", png, sma_plot_surv_dx_dx)
	sma_plot_file("survival_asct_dx.png", png, sma_plot_surv_asct_dx)
	sma_plot_file("survival_sm.png", png, sma_plot_surv_sm)
}