summary.crossnma.model {crossnma}  R Documentation 
Summary function for crossnma.model object
## S3 method for class 'crossnma.model'
summary(object, ...)
object 
An object generated by the

... 
Additional arguments (ignored) 
An object of classes summary.crossnma.model
and
crossnma.model
containing information on the JAGS model,
which is a list containing the following components:
model 
A long character string containing JAGS code that
will be run in 
data 
The data to be used to run JAGS model. 
trt.key 
A table of the treatments and its mapped integer number (as used in JAGS model). 
study.key 
A table of the studies and its mapped integer number (as used in JAGS model). 
trt.effect 
A character defining the model for the studyspecific treatment effects. 
method.bias 
A character for defining the method to combine randomized clinical trials (RCT) and nonrandomized studies (NRS). 
covariate 
A vector of the the names of the covariates
( 
cov.ref 
A vector of values of 
dich.cov.labels 
A matrix with the levels of each dichotomous covariate and the corresponding assigned 0 / 1 values. 
split.regcoef 
A logical value. If FALSE the within and betweenstudy regression coefficients will be considered equal. 
regb.effect 
A character indicating the model for the betweenstudy regression coefficients across studies. 
regw.effect 
A character indicating the model for the withinstudy regression coefficients across studies. 
bias.effect 
A character indicating the model for the bias coefficients across studies. 
bias.type 
A character indicating the effect of bias on the treatment effect; additive ('add') or multiplicative ('mult') or both ('both'). 
all.data.ad 
A data.frame object with the prt.data (after it is aggregated) and std.data in a single dataset. 
call 
Function call. 
version 
Version of R package crossnma used to create object. 
Guido Schwarzer sc@imbi.unifreiburg.de
# We conduct a network metaanalysis assuming a randomeffects
# model.
# The data comes from randomizedcontrolled trials and
# nonrandomized studies (combined naively)
head(ipddata) # participantlevel data
head(stddata) # studylevel data
# Create a JAGS model
mod < crossnma.model(treat, id, relapse, n, design,
prt.data = ipddata, std.data = stddata,
reference = "A", trt.effect = "random", method.bias = "naive")
summary(mod)