bayesmeta (version 3.4)

GoralczykEtAl2011: Liver transplant example data

Description

Numbers of cases (transplant patients) and events (acute rejections, steroid resistant rejections, and deaths) in experimental and control groups of 19 studies.

Usage

data("GoralczykEtAl2011")

Format

The data frame contains the following columns:

publication	character	publication identifier (first author and publication year)
year	numeric	publication year
randomized	factor	randomization status (yes / no / not stated)
control.type	factor	type of control group (‘concurrent’ or ‘historical’)
comparison	factor	type of comparison (‘IL-2RA only’, ‘delayed CNI’, or ‘no/low steroids’)
IL2RA	factor	type of interleukin-2 receptor antagonist (IL-2RA) (‘basiliximab’ or ‘daclizumab’)
CNI	factor	type of calcineurin inhibitor (CNI) (‘tacrolimus’ or ‘cyclosporine A’)
MMF	factor	use of mycofenolate mofetil (MMF) (y/n)
followup	numeric	follow-up time in months
treat.AR.events	numeric	number of AR events in experimental group
treat.SRR.events	numeric	number of SRR events in experimental group
treat.deaths	numeric	number of deaths in experimental group
treat.total	numeric	number of cases in experimental group
control.AR.events	numeric	number of AR events in control group
control.SRR.events	numeric	number of SRR events in control group
control.deaths	numeric	number of deaths in control group
control.total	numeric	number of cases in control group

Details

A systematic literature review investigated the evidence on the effect of Interleukin-2 receptor antagonists (IL-2RA) and resulted in 19 controlled studies reporting acute rejection (AR) and steroid-resistant rejection (SRR) rates as well as mortality in adult liver transplant recipients.

See Also

CrinsEtAl2014.

Examples

data("GoralczykEtAl2011")
if (FALSE) {
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
require("metafor")
goralczyk.es <- escalc(measure="OR",
                       ai=exp.AR.events,  n1i=exp.total,
                       ci=cont.AR.events, n2i=cont.total,
                       slab=publication, data=GoralczykEtAl2011)
print(goralczyk.es[,c(1,10,12,13,15,16,17)])

# analyze using weakly informative half-Cauchy prior for heterogeneity:
goralczyk.ma <- bayesmeta(goralczyk.es, tau.prior=function(t){dhalfcauchy(t,scale=1)})

# show summary:
print(goralczyk.ma)

# show forest plot:
forestplot(goralczyk.ma)
}