CrinsEtAl2014 {bayesmeta}R Documentation

Pediatric liver transplant example data


Numbers of cases (transplant patients) and events (acute rejections, steroid resistant rejections, PTLDs, and deaths) in experimental and control groups of six 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 (y/n)
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 numeric number of AR events in experimental group numeric number of SRR events in experimental group numeric number of PTLD events in experimental group
treat.deaths numeric number of deaths in experimental group numeric number of cases in experimental group numeric number of AR events in control group numeric number of SRR events in control group numeric number of PTLD events in control group
control.deaths numeric number of deaths in control group 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 six controlled studies reporting acute rejection (AR), steroid-resistant rejection (SRR) and post-transplant lymphoproliferative disorder (PTLD) rates as well as mortality in pediatric liver transplant recipients.


N.D. Crins, C. Roever, A.D. Goralczyk, T. Friede. Interleukin-2 receptor antagonists for pediatric liver transplant recipients: A systematic review and meta-analysis of controlled studies. Pediatric Transplantation, 18(8):839-850, 2014. doi:10.1111/petr.12362.


C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. doi:10.18637/jss.v093.i06.

C. Roever, T. Friede. Using the bayesmeta R package for Bayesian random-effects meta-regression. Computer Methods and Programs in Biomedicine, 299:107303, 2023. doi:10.1016/j.cmpb.2022.107303.

T.G. Heffron et al. Pediatric liver transplantation with daclizumab induction therapy. Transplantation, 75(12):2040-2043, 2003. doi:10.1097/01.TP.0000065740.69296.DA.

N.E.M. Gibelli et al. Basiliximab-chimeric anti-IL2-R monoclonal antibody in pediatric liver transplantation: comparative study. Transplantation Proceedings, 36(4):956-957, 2004. doi:10.1016/j.transproceed.2004.04.070.

S. Schuller et al. Daclizumab induction therapy associated with tacrolimus-MMF has better outcome compared with tacrolimus-MMF alone in pediatric living donor liver transplantation. Transplantation Proceedings, 37(2):1151-1152, 2005. doi:10.1016/j.transproceed.2005.01.023.

R. Ganschow et al. Long-term results of basiliximab induction immunosuppression in pediatric liver transplant recipients. Pediatric Transplantation, 9(6):741-745, 2005. doi:10.1111/j.1399-3046.2005.00371.x.

M. Spada et al. Randomized trial of basiliximab induction versus steroid therapy in pediatric liver allograft recipients under tacrolimus immunosuppression. American Journal of Transplantation, 6(8):1913-1921, 2006. doi:10.1111/j.1600-6143.2006.01406.x.

J.M. Gras et al. Steroid-free, tacrolimus-basiliximab immunosuppression in pediatric liver transplantation: Clinical and pharmacoeconomic study in 50 children. Liver Transplantation, 14(4):469-477, 2008. doi:10.1002/lt.21397.

See Also



## Not run: 
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
require("metafor") <- escalc(measure="OR",
                   slab=publication, data=CrinsEtAl2014)

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

# show results:

# show heterogeneity posterior along with prior:
plot(, which=4, prior=TRUE)

# perform meta analysis using 2 randomized studies only
# but use 4 non-randomized studies to inform heterogeneity prior:
crins.nrand <- bayesmeta([$randomized=="no",],
crins.rand  <- bayesmeta([$randomized=="yes",],
plot(crins.nrand, which=4, prior=TRUE,
     main="non-randomized posterior = randomized prior")
plot(crins.rand, which=4, prior=TRUE, main="randomized posterior")
plot(crins.rand, which=1)

## End(Not run)

[Package bayesmeta version 3.4 Index]