Trsnalte winBug model to get rstamarm to get the same results

Dear All,
how I can translate this winBug model for studying the treatment effect to rstanarm interface, using rstanarm package taking into account all the parameters defined below:
and thanks in advance:

> mydata = data.frame(treat = c( rep(0, 120), rep(1, 150), rep(0, 40), rep(1, 30)),
+                     death = c( rep(0, 120), rep(0, 150), rep(1, 40), rep(1, 30) ) )
>                              # nc-yc        nt-yt        yc        # yt
> 
> str(mydata)
'data.frame':	340 obs. of  2 variables:
 $ treat: num  0 0 0 0 0 0 0 0 0 0 ...
 $ death: num  0 0 0 0 0 0 0 0 0 0 ...
> 
> 
> winBugModel <- function(){
+   yt ~ dbin(p1, nt)
+   yc ~ dbin(pc, nc)
+   pc ~ dunif(0,1)
+   eta <- log(pc)
+   log(p1) <- eta + coef
+   coef ~ dnorm(meanPrior, sigmaPrior)
+   RR <- exp(coef)
+ }
> 
> initial <- function(){
+   list(pc = runif(1,0,1),
+        coef = runif(1, -2,0))
+ }
> 
> 
> 
> dataTrail <- list(yt = 30, 
+                   nt = 180, 
+                   yc = 40, 
+                   nc = 160, 
+                   meanPrior = 0,
+                   sigmaPrior = 1/200
+ )
> 
> output <- jags(data = dataTrail,
+                inits = initial, 
+                parameters.to.save = c("RR"),
+                model.file = winBugModel,
+                n.chains = 4,
+                n.iter = 20000,
+                n.thin = 2)
Compiling model graph
   Resolving undeclared variables
   Allocating nodes
Graph information:
   Observed stochastic nodes: 2
   Unobserved stochastic nodes: 2
   Total graph size: 14

Initializing model

  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
  |**************************************************| 100%
> 
> 
> OUTPUTS <- rbind(output$BUGSoutput$sims.array[,1,],
+                  output$BUGSoutput$sims.array[,2,],
+                  output$BUGSoutput$sims.array[,3,],
+                  output$BUGSoutput$sims.array[,4,])
> 
> RR = mean(OUTPUTS[,1])
> 
> c(RR = mean(OUTPUTS[,1]),  quantile(OUTPUTS[,1], 0.025), quantile(OUTPUTS[,1],0.975))
       RR      2.5%     97.5% 
0.6669388 0.4223434 0.9914351 
> 
> 100*mean( (1-OUTPUTS[,1])>0 )
[1] 97.675
> 
> 100*mean( (0.5-OUTPUTS[,1])>0 )
[1] 11.215

I’ve never used WinBugs, but the model you’ve posted doesn’t look like a generalized linear model and so cannot be IMHO directly expressed in rstanarm. You should be able to express it in Stan, although the model is a bit weird - in particular it looks like it allows p1 > 1 (and I assume p1 is supposed to be a probability) and RR appears to be only assigned and unused. So I would be slighlty suspicious whether the model is actually correct/useful.