cace.meta.c {BayesCACE}  R Documentation 
This function performs the Bayesian hierarchical model method for metaanalysis when the dataset has complete compliance information for all studies, as described in Section 2.2, "the Bayesian hierarchical model", of the package manuscript.
cace.meta.c(
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 
an input dataset with the same structure as the example data 
param 
a character string vector indicating the parameters to be tracked and estimated.
By default the following parameters (see 
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

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.
Lunn D, Jackson C, Best N, Thomas A, Spiegelhalter D (2012). The BUGS book: A practical introduction to Bayesian analysis. CRC press.
Zeger SL, Liang K, Albert PS (1988). “Models for longitudinal data: a generalized estimating equation approach.” Biometrics, 1049–1060.
data("epidural_c", package = "BayesCACE")
set.seed(123)
out.meta.c < cace.meta.c(data = epidural_c, conv.diag = TRUE,
mcmc.samples = TRUE, study.specific = TRUE)
# By calling the object smry from the output list out.meta.c, posterior estimates
# (posterior mean, standard deviation, posterior median, 95\% credible interval, and
# timeseries standard error) are displayed.
out.meta.c$smry
out.meta.c$DIC