fit1 <- survfit(Surv(dat$diff_in_days, dat$survivalstat)~Schedule,data = dat)

#turn into df
df <- broom::tidy(fit1)

fit_df <- df  %>%
  #group by strata
  group_by(strata) %>%
  #get day  of interest or day before it
  filter(time <= 365) %>%
  arrange(time) %>%
  # pull last date
  do(tail(.,1))

#calculate z score based on 2 sample test at that time point
z <- (fit_df$estimate[1]-fit_df$estimate[2]) / (sqrt( fit_df$std.error[1]^2+ fit_df$std.error[2]^2))
#get probability of z score
pz <- pnorm(abs(z))
#get p value
pvalue <- round(2 * (1-pz),2)

ggsurv <- ggsurvplot(
  fit1,
  data = dat,

  pval = TRUE,
  pval.size = 4,
  pval.method = TRUE,
  pval.method.size = 3,
  log.rank.weights = "1",

  conf.int = TRUE,
  conf.int.style = "ribbon",
  conf.int.alpha = 0.2,

  xlab = "Time in days",

  tables.theme = theme_cleantable(),
  ggtheme = theme_bw()
)

ggsurv$plot <- ggsurv$plot +
                geom_vline(aes(xintercept=365))+
                geom_text(aes(x = 500,y=.8,label = paste0("p = " ,pvalue) ))

print(ggsurv)