Hi I am trying to replicate the winbugs code (posted earlier at https://groups.google.com/forum/#!topic/stan-users/yKOOfY7hGVg) in Stan. Ii runs relatively fast, but I am getting warning on “Bayesian Fraction of Missing Information was low” for all the chains. I would appreciate if you could help to re-parametrize the model. i tried to non-centered it (see below) but I am still getting warnings… i am attaching the database that I used.
The BUGS code is
model{
for(i in 1:ns){ # LOOP THROUGH STUDIES
w[i,1] <- 0 # adjustment for multi-arm trials is zero for control arm
delta[i,1] <- 0 # treatment effect is zero for control arm
mu[i] ~ dnorm(0,.0001) # vague priors for all trial baselines
for (k in 1:na[i]) { # LOOP THROUGH ARMS
r[i,k] ~ dbin(p[i,k],n[i,k]) # binomial likelihood
logit(p[i,k]) <- mu[i] + delta[i,k] # model for linear predictor
rhat[i,k] <- p[i,k] * n[i,k] # expected value of the numerators
}
for (k in 2:na[i]) { # LOOP THROUGH ARMS
# trial-specific LOR distributions
delta[i,k] ~ dnorm(md[i,k],taud[i,k])
# mean of LOR distributions (with multi-arm trial correction)
md[i,k] <- d[t[i,k]] - d[t[i,1]] + sw[i,k]
# precision of LOR distributions (with multi-arm trial correction)
taud[i,k] <- tau *2*(k-1)/k
# adjustment for multi-arm RCTs
w[i,k] <- (delta[i,k] - d[t[i,k]] + d[t[i,1]])
# cumulative adjustment for multi-arm trials
sw[i,k] <- sum(w[i,1:k-1])/(k-1)
}
}
d[1]<-0 # treatment effect is zero for reference treatment
# vague priors for treatment effects
for (k in 2:nt){ d[k] ~ dnorm(0,.0001) }
sd ~ dunif(0,5) # vague prior for between-trial SD
tau <- pow(sd,-2) # between-trial precision = (1/between-trial variance)
}
The STAN code is
data {
int<lower=1> ND; // Number of data points
int<lower=1> NT; // Number of treatments
int<lower=1> NS; // Number of studies
int<lower=0> sampleSize[ND]; // Number of patients in study and treatment arm
int<lower=0> responders[ND]; // Number of responders in study and treatment arm
int<lower=1, upper=NT> treatment[ND]; // Treatment used in study and treatment arm
int<lower=1, upper=NS> study[ND]; // Study
int<lower=1, upper=NT> treatment_base[ND]; // The first or "base-case" treatment per study
}
parameters {
vector[NT] d; // Treatment effect for each treatment
vector[NS] mu; // One mu for each study
vector[ND] delta;
real<lower=0> sigmasq_delta;
}
transformed parameters {
vector[NT] d_new=d; // Treatment effect for each treatment with values of 0 for treatment one
real<lower=0> sigma_delta=sqrt(sigmasq_delta);
real w[NS,NT]=rep_array(0.0, NS, NT);
vector[ND] sw_vector;
vector[ND] vard_vector;
vector[ND] sqrt_vard_vector;
vector[ND] delta_new=delta;
vector[ND] md;
d_new[1] = 0.0;
for (i in 1:ND) {
if (treatment[i] == 1) {
delta_new[i] = 0.0;
w[study[i],treatment[i]] = 0.0;
vard_vector[i] = 1E-2;
sw_vector[i] = 0.0;
} else {
w[study[i],treatment[i]] = delta_new[i] - d_new[treatment[i]] + d_new[treatment_base[i]];
vard_vector[i] = (pow(sigma_delta,2) * treatment[i])/(2 * (treatment[i]-1));
sw_vector[i] = sum(w[study[i],1:(treatment[i]-1)])/(treatment[i]-1);
}
sqrt_vard_vector[i] = sqrt(vard_vector[i]);
}
md[1:ND] = d_new[treatment[1:ND]] - d_new[treatment_base[1:ND]] + sw_vector[1:ND];
}
model {
responders[1:ND] ~ binomial_logit(sampleSize[1:ND], mu[study[1:ND]] + delta_new[1:ND]);
delta[1:ND] ~ normal(md[1:ND], sqrt_vard_vector[1:ND]);
mu[study[1:ND]] ~ normal(0, 10);
d[treatment[1:ND]] ~ normal(0, 10);
sigmasq_delta ~ uniform(0, 10);
}
I tried non-centering of
delta[1:ND] ~ normal(md[1:ND], sqrt_vard_vector[1:ND]);
with introduction of zeta ~N(0,1) parameter
data {
int<lower=1> ND; // Number of data points
int<lower=1> NT; // Number of treatments
int<lower=1> NS; // Number of studies
int<lower=0> sampleSize[ND]; // Number of patients in study and treatment arm
int<lower=0> responders[ND]; // Number of responders in study and treatment arm
int<lower=1, upper=NT> treatment[ND]; // Treatment used in study and treatment arm
int<lower=1, upper=NS> study[ND]; // Study
int<lower=1, upper=NT> treatment_base[ND]; // The first or "base-case" treatment per study
}
parameters {
vector[NT] d; // Treatment effect for each treatment
vector[NS] mu; // One mu for each study
vector[ND] delta;
real<lower=0> sigmasq_delta;
real zeta[ND];
}
transformed parameters {
vector[NT] d_new=d; // Treatment effect for each treatment with values of 0 for treatment one
real<lower=0> sigma_delta=sqrt(sigmasq_delta);
real w[NS,NT]=rep_array(0.0, NS, NT);
vector[ND] sw_vector;
vector[ND] vard_vector;
vector[ND] sqrt_vard_vector;
vector[ND] delta_new=delta;
d_new[1] = 0.0;
for (i in 1:ND) {
if (treatment[i] == 1) {
delta_new[i] = 0.0; // Set delta_new to zero for control treatment
w[study[i],treatment[i]] = 0.0;
vard_vector[i] = 1E-2;
sw_vector[i] = 0.0;
} else {
w[study[i],treatment[i]] = delta_new[i] - d_new[treatment[i]] + d_new[treatment_base[i]]; // adjustment for multi-arm RCTs
vard_vector[i] = (pow(sigma_delta,2) * treatment[i])/(2 * (treatment[i]-1));
sw_vector[i] = sum(w[study[i],1:(treatment[i]-1)])/(treatment[i]-1);
}
sqrt_vard_vector[i] = sqrt(vard_vector[i])*zeta[i];
}
delta_new[1:ND] = d_new[treatment[1:ND]] - d_new[treatment_base[1:ND]] + sw_vector[1:ND]+sqrt_vard_vector[1:ND];
}
model {
responders[1:ND] ~ binomial_logit(sampleSize[1:ND], mu[study[1:ND]] + delta_new[1:ND]);
zeta~normal(0,1);
mu[study[1:ND]] ~ normal(0, 10);
d[treatment[1:ND]] ~ normal(0, 10);
sigmasq_delta ~ uniform(0, 10);
}
library(rstan)
library(rstantools)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
data = read.csv("Anticoagulant.csv", header=TRUE)
ND =nrow(data)
NT=max(data$treatment)
NS=max(data$study)
stan.dat <- list(ND=ND,NT=NT,NS=NS, sampleSize=data$sampleSize,
responders=data$responders,treatment=data$treatment,
study=data$study, treatment_base=data$treatment_base)
logit.nma <- stan_model(file='NMA_binary.stan',
auto_write = rstan_options(auto_write = TRUE))
set.seed(1234)
MakeInits <- function(){
list( mu = rnorm(NS, 0,1),
d = rnorm(NT,0,1),
delta = rnorm(ND, 0,1),
sigmasq_delta = runif(1,0,2))
}
t1=proc.time()
fit_nma <- sampling( logit.nma, data = stan.dat, warmup=1000, iter = 3000, seed=1234,
chains =3, pars=c('delta_new', 'sigma_delta'))
t_stan=proc.time()-t1
```<a class="attachment" href="//cdck-file-uploads-global.s3.dualstack.us-west-2.amazonaws.com/standard14/uploads/mc_stan/original/2X/6/62fb7ffee2b85aa2c33ae85f3acc20693251ad63.csv">Anticoagulant.csv</a> (1.8 KB)