bayesmeta (version 3.4)

CrinsEtAl2014: Pediatric liver transplant example data

Description

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

Usage

data("CrinsEtAl2014")

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 (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
treat.AR.events	numeric	number of AR events in experimental group
treat.SRR.events	numeric	number of SRR events in experimental group
treat.PTLD.events	numeric	number of PTLD 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.PTLD.events	numeric	number of PTLD 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 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.

References

C. Roever. Bayesian random-effects meta-analysis using the bayesmeta R package. Journal of Statistical Software, 93(6):1-51, 2020. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1002/lt.21397").

See Also

GoralczykEtAl2011.

Examples

data("CrinsEtAl2014")
if (FALSE) {
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
require("metafor")
crins.es <- escalc(measure="OR",
                   ai=exp.AR.events,  n1i=exp.total,
                   ci=cont.AR.events, n2i=cont.total,
                   slab=publication, data=CrinsEtAl2014)
print(crins.es)

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

# show results:
print(crins.ma)
forestplot(crins.ma)
plot(crins.ma)

# show heterogeneity posterior along with prior:
plot(crins.ma, 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(crins.es[crins.es$randomized=="no",],
                         tau.prior=function(t){dhalfcauchy(t,scale=1)})
crins.rand  <- bayesmeta(crins.es[crins.es$randomized=="yes",],
                         tau.prior=function(t){crins.nrand$dposterior(tau=t)})
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)
}