Include weibull fit in ggsurvplot - r

Question

I would like to fit a weibull curve to some event data and then include the fitted weibull curve in a survival plot plotted by survminer::ggsurvplot. Any ideas of how?
Here is an example to work on:
A function for simulating weibull data:
# N = sample size
# lambda = scale parameter in h0()
# rho = shape parameter in h0()
# beta = fixed effect parameter
# rateC = rate parameter of the exponential distribution of C
simulWeib <- function(N, lambda, rho, beta, rateC)
{
# covariate --> N Bernoulli trials
x <- sample(x=c(0, 1), size=N, replace=TRUE, prob=c(0.5, 0.5))
# Weibull latent event times
v <- runif(n=N)
Tlat <- (- log(v) / (lambda * exp(x * beta)))^(1 / rho)
# censoring times
C <- rexp(n=N, rate=rateC)
# follow-up times and event indicators
time <- pmin(Tlat, C)
status <- as.numeric(Tlat <= C)
# data set
data.frame(id=1:N,
time=time,
status=status,
x=x)
}
generate data
set.seed(1234)
betaHat <- rep(NA, 1e3)
for(k in 1:1e3)
{
dat <- simulWeib(N=100, lambda=0.01, rho=1, beta=-0.6, rateC=0.001)
fit <- coxph(Surv(time, status) ~ x, data=dat)
betaHat[k] <- fit$coef
}
#Estimate a survival function
survfit(Surv(as.numeric(time), x)~1, data=dat) -> out0
#plot
library(survminer)
ggsurvplot(out0, data = dat, risk.table = TRUE)
gg1 <- ggsurvplot(
out0, # survfit object with calculated statistics.
data = dat, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimaes of survival curves.
xlim = c(0,2000), # present narrower X axis, but not affect
# survival estimates.
break.time.by = 500, # break X axis in time intervals by 500.
ggtheme = theme_minimal(), # customize plot and risk table with a theme.
risk.table.y.text.col = T, # colour risk table text annotations.
risk.table.y.text = FALSE,
surv.median.line = "hv",
color = "darkgreen",
conf.int.fill = "lightblue",
title = "Survival probability",# show bars instead of names in text annotations
# in legend of risk table
)
gg1

Answer 1

As far as I see this, it is not possible do it with ggsurvplot at this moment.
I created an issue requesting this feature: https://github.com/kassambara/survminer/issues/276
You can plot survivor curves of a weibull model with ggplot2 like this:
library("survival")
wbmod <- survreg(Surv(time, status) ~ x, data = dat)
s <- seq(.01, .99, by = .01)
t_0 <- predict(wbmod, newdata = data.frame(x = 0),
type = "quantile", p = s)
t_1 <- predict(wbmod, newdata = data.frame(x = 1),
type = "quantile", p = s)
smod <- data.frame(time = c(t_0, t_1),
surv = rep(1 - s, times = 2),
strata = rep(c(0, 1), each = length(s)),
upper = NA, lower = NA)
head(surv_summary(cm))
library("ggplot2")
ggplot() +
geom_line(data = smod, aes(x = time, y = surv, color = factor(strata))) +
theme_classic()
However to my knowledge you cannot use survminer (yet):
library("survminer")
# wrong:
ggsurvplot(smod)
# does not work:
gg1$plot + geom_line(data = smod, aes(x = time, y = surv, color = factor(strata)))

Answer 2

The following works for me. Probably the credit goes to Heidi filling a feature request.
Hope, someone finds this useful.
library(survminer)
library(tidyr)
s <- with(lung,Surv(time,status))
sWei <- survreg(s ~ as.factor(sex),dist='weibull',data=lung)
fKM <- survfit(s ~ sex,data=lung)
pred.sex1 = predict(sWei, newdata=list(sex=1),type="quantile",p=seq(.01,.99,by=.01))
pred.sex2 = predict(sWei, newdata=list(sex=2),type="quantile",p=seq(.01,.99,by=.01))
df = data.frame(y=seq(.99,.01,by=-.01), sex1=pred.sex1, sex2=pred.sex2)
df_long = gather(df, key= "sex", value="time", -y)
p = ggsurvplot(fKM, data = lung, risk.table = T)
p$plot = p$plot + geom_line(data=df_long, aes(x=time, y=y, group=sex))