Reputation: 349
I am creating a Kaplan-Meier plot that shows the relationship between family history of mental illness and mental illness onset using ggsurvplot from the survminer package. This is the code I have used:
km_fhr <- ggsurvplot(fit = survfit(Surv(fu_time, smidg) ~ fhr, data = df),
legend.labs = c("Control", "Family high-risk"),
legend.title = "",
censor.shape = 124,
censor.size = 2.5,
palette = c("#00ABE7", "#FFA69E")) +
labs(x = "Follow-up time (years)", y = "Probability of no SMI diagnosis")
The Kaplan-Meier curve looks like this:
It seems to me that it would be more intuitive to plot risk of illness (failure rate) rather than probability of no illness (survival rate) on the y-axis. I assume there is a simple way to do this, but I have not been able to find a description in the survminer documentation.
Thank you in advance!
Upvotes: 4
Views: 2196
Reputation: 3438
Use the argument fun = event
for cumulative events. Reproducible example:
library(survminer)
ggsurvplot(
survfit(
Surv(time, status) ~ sex + rx + adhere,
data = colon
),
fun = "event"
)
From the help for ggsurvplot
:
fun: an arbitrary function defining a transformation of the survival curve. Often used transformations can be specified with a character argument: "event" plots cumulative events (f(y) = 1-y), "cumhaz" plots the cumulative hazard function (f(y) = -log(y)), and "pct" for survival probability in percentage.
Upvotes: 5