cace.meta.ic {BayesCACE}  R Documentation 
This function also estimates \theta^{\mathrm{CACE}}
using the Bayesian hierarchcal model
but can accommodate studies with incomplete compliance data.
The necessary data structure and the likelihood function are presented in Section 2.3 of the
package manuscript, "CACE for metaanalysis with incomplete compliance information".
cace.meta.ic(
data,
param = c("CACE", "u1out", "v1out", "s1out", "b1out", "pic", "pin", "pia"),
random.effects = list(),
re.values = list(),
model.code = "",
digits = 3,
n.adapt = 1000,
n.iter = 1e+05,
n.burnin = floor(n.iter/2),
n.chains = 3,
n.thin = max(1, floor((n.iter  n.burnin)/1e+05)),
conv.diag = FALSE,
mcmc.samples = FALSE,
study.specific = FALSE
)
data 
a input dataset the same structure as the example data 
param 
the list of parameter used. Default to 
random.effects 
a list of logical values indicating whether random effects are included in the model.
The list should contain the assignment for these parameters only: 
re.values 
a list of parameter values for the random effects. It should contain the assignment for these
parameters only: 
model.code 
a string representation of the model code; each line should be separated. Default to constructing
model code using the 
digits 
number of digits. Default to 
n.adapt 
adapt value. Default to 
n.iter 
number of iterations. Default to 
n.burnin 
number of burnin iterations. Default to 
n.chains 
number of chains. Default to 
n.thin 
thinning rate, must be a positive integer. Default to 
conv.diag 
whether or not to show convergence diagnostics. Default to 
mcmc.samples 
whether to include JAGS samples in the final output. Default to 
study.specific 
a logical value indicating whether to calculate the studyspecific

Note that when compiling the JAGS
model, the warning ‘adaptation incomplete’ may
occasionally occur, indicating that the number of iterations for the adaptation process
is not sufficient. The default value of n.adapt
(the number of iterations for adaptation)
is 1,000. This is an initial sampling phase during which the samplers adapt their behavior
to maximize their efficiency (e.g., a Metropolis–Hastings random walk algorithm may change
its step size). The ‘adaptation incomplete’ warning indicates the MCMC algorithm may not
achieve maximum efficiency, but it generally has little impact on the posterior estimates
of the treatment effects. To avoid this warning, users may increase n.adapt
.
It returns a model object whose attribute type is cace.Bayes
Zhou J, Hodges JS, Suri MFK, Chu H (2019). “A Bayesian hierarchical model estimating CACE in metaanalysis of randomized clinical trials with noncompliance.” Biometrics, 75(3), 978–987.
data("epidural_ic", package = "BayesCACE")
set.seed(123)
out.meta.ic < cace.meta.ic(data = epidural_ic, conv.diag = TRUE,
mcmc.samples = TRUE, study.specific = TRUE)