BucherEtAl1997 {bayesmeta} | R Documentation |

## Direct and indirect comparison example data

### Description

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

### Usage

`data("BucherEtAl1997")`

### Format

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 |

### Details

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).

### Source

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.

### References

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.

### Examples

```
# load data:
data("BucherEtAl1997")
# show data:
head(BucherEtAl1997)
## 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:
print(bmr01)
# 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)
print(fmr01)
# compare Bayesian and frequentist results;
# estimated log-OR for "TMP-SMX" vs. "D/P"
rbind("bayesmeta"=bmr01$summary[c("mean","sd"),"TMP.DP"],
"rma" =c(fmr01$beta["TMP.DP",], fmr01$se[1]))
# estimated log-OR for "AP" vs. "D/P"
rbind("bayesmeta"=bmr01$summary[c("mean","sd"),"AP.DP"],
"rma" =c(fmr01$beta["AP.DP",], fmr01$se[2]))
# estimated heterogeneity:
rbind("bayesmeta"=bmr01$summary["median","tau"],
"rma" =sqrt(fmr01$tau2))
## End(Not run)
```

*bayesmeta*version 3.4 Index]