i perform both a classic survival analysis with the function survreg of the survival package and a bayesian analysis with rstan. i believe that there is no significant difference between the two approaches. so, how to transform the result of rstan to obtain the output similar to the result of frequentist approach.

the data is from the survival package. the codes is as follows,

```
rm(list=ls())
library(survival)
data_for_Stan <- list(n_cen = with(ovarian,sum(fustat)),
n_not_cen = with(ovarian,sum(1 - fustat)),
t_cen = with(ovarian,futime[fustat == 1]),
t_not_cen = with(ovarian,futime[fustat == 0]),
x_cen = ovarian %>% mutate(intercept = 1) %>%
filter(fustat == 1) %>%
dplyr::select(intercept,ecog.ps,rx),
x_not_cen = ovarian %>% mutate(intercept = 1) %>%
filter(fustat == 0) %>%
dplyr::select(intercept,ecog.ps,rx),
K= 3)
##Model with Bayessian framework
library(rstan)
Bayesian_model <-
"
data {
int < lower = 1 > n_cen;
int < lower = 1 > n_not_cen;
int < lower = 1 > K;
vector[n_cen] t_cen;
vector[n_not_cen] t_not_cen;
matrix[n_cen,K] x_cen;
matrix[n_not_cen,K] x_not_cen;
}
parameters {
vector[K] beta;
real < lower = 0 > rho;
}
model {
to_vector(beta) ~ normal(0,100^2);
target += cauchy_lpdf(rho|1.0, 10.0);
target += weibull_lpdf(t_not_cen | rho, exp(x_not_cen * beta));
target += weibull_lccdf(t_cen | rho, exp(x_cen * beta));
}
"
write(Bayesian_model,file = "Bayesian_model.stan")
fit_predict = stan(file = "Bayesian_model.stan",
data = data_for_Stan, warmup = 1000,
iter = 2000, chains = 4,
cores = 2, thin = 1)
fit <- survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull')
fit_predict
summary(fit)$table
```

my motivation is from the work of julia stander et al.(A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?The American Statistician,2017)

my bayesian model is

Any help is appriciated!