GoralczykEtAl2011 {bayesmeta} | R Documentation |
Numbers of cases (transplant patients) and events (acute rejections, steroid resistant rejections, and deaths) in experimental and control groups of 19 studies.
data("GoralczykEtAl2011")
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.
data("GoralczykEtAl2011") ## Not run: # 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) ## End(Not run)