BucherEtAl1997 {bayesmeta}R Documentation

Direct and indirect comparison example data


Numbers of subjects and events in the different treatment arms of 22 studies.




The data frame contains the following columns:

study character publication identifier (first author and publication year)
treat.A factor treatment in first study arm (“TMP-SMX” or “AP”)
treat.B factor treatment in second study arm (“D/P” or “AP”)
events.A numeric number of events in first study arm
events.B numeric number of events in second study arm
total.A numeric total number of patients in first study arm
total.B numeric total number of patients in second study arm


Bucher et al. (1997) discussed the example case of the comparison of sulphametoxazole-trimethoprim (TMP-SMX) versus dapsone/pyrimethamine (D/P) for the prophylaxis of Pneumocystis carinii pneumonia in HIV patients. Eight studies had undertaken a head-to-head comparison of both medications, but an additional 14 studies were available investigating one of the two medications with aerosolized pentamidine (AP) as a comparator. Nine studies compared TMP-SMX vs. AP, and five studies compared D/P vs. AP. Together these provide indirect evidence on the effect of TMP-SMX compared to D/P (Kiefer et al., 2015).

The example constitutes a simple case of a network meta-analysis (NMA) setup, where only two-armed studies are considered, and analysis is based on pairwise comparisons of treatments (or contrasts). In this case, the joint analysis of direct and indirect evidence may be implemented as a special case of a meta-regression (Higgins et al., 2019; Sec. 11.4.2). The original data in fact included some three-armed studies, in which case one of the arms was deliberately omitted (Bucher et al.; 1997).


H.C. Bucher, G.H. Guyatt, L.E. Griffith, S.D. Walter. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology, 50(6):683-691, 1997. doi:10.1016/S0895-4356(97)00049-8.


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.

J.P.T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M.J. Page, V.A. Welch (eds.). Cochrane handbook for systematic reviews of interventions. Wiley and Sons, 2nd edition, 2019. doi:10.1002/9781119536604. http://training.cochrane.org/handbook.

C. Kiefer, S. Sturtz, R. Bender. Indirect comparisons and network meta-analyses. Deutsches Aerzteblatt International, 112(47):803-808, 2015. doi:10.3238/arztebl.2015.0803.


# load data:

# show data:

## Not run: 
# compute effect sizes (log-ORs for pairwise comparisons)
# from the count data:
es <- escalc(measure="OR",
             ai=events.A, n1i=total.A,   # "exposure group"
             ci=events.B, n2i=total.B,   # "control group"
             slab=study, data=BucherEtAl1997)

# specify regressor matrix:
X <- cbind("TMP.DP" = rep(c(1, 0, 1), c(8,5,9)),
           "AP.DP"  = rep(c(0, 1,-1), c(8,5,9)))

# perform Bayesian meta-regression:
bmr01 <- bmr(es, X=X)

# show default output:

# specify contrast matrix:
contrastX <- rbind("TMP-SMX vs. D/P"=c(1,0),
                   "AP vs. D/P"     =c(0,1),
                   "TMP-SMX vs. AP" =c(1,-1))
# show summary including contrast estimates:
summary(bmr01, X.mean=contrastX)
# show forest plot including contrast estimates:
forestplot(bmr01, X.mean=contrastX, xlab="log-OR")

# perform frequentist meta-regression:
fmr01 <- rma(es, mods=X, intercept=FALSE)

# compare Bayesian and frequentist results;
# estimated log-OR for "TMP-SMX" vs. "D/P"
      "rma"      =c(fmr01$beta["TMP.DP",], fmr01$se[1]))

# estimated log-OR for "AP" vs. "D/P"
      "rma"      =c(fmr01$beta["AP.DP",], fmr01$se[2]))

# estimated heterogeneity:
      "rma"      =sqrt(fmr01$tau2))

## End(Not run)

[Package bayesmeta version 3.4 Index]