GoralczykEtAl2011 {bayesmeta}R Documentation

Liver transplant example data


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




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


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.


A.D. Goralczyk, N. Hauke, N. Bari, T.Y. Tsui, T. Lorf, A. Obed. Interleukin-2 receptor antagonists for liver transplant recipients: A systematic review and meta-analysis of controlled studies. Hepatology, 54(2):541-554, 2011. doi:10.1002/hep.24385.

See Also



## Not run: 
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
goralczyk.es <- escalc(measure="OR",
                       ai=exp.AR.events,  n1i=exp.total,
                       ci=cont.AR.events, n2i=cont.total,
                       slab=publication, data=GoralczykEtAl2011)

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

# show summary:

# show forest plot:

## End(Not run)

[Package bayesmeta version 3.4 Index]