pairwise: Pairwise meta-analyses for all treatment pairs with direct...

In NMA: Network Meta-Analysis Based on Multivariate Meta-Analysis and Meta-Regression Models

Pairwise meta-analyses for all treatment pairs with direct comparisons on the network

Description

Pairwise meta-analyses for all treatment pairs with direct comparisons on the network are performed. The synthesis analyses are performed by rma and regtest in metafor package.

Usage

pairwise(x,method="SJ",test="knha")

Arguments

x	Output object of setup
method	Method of the estimation of pairwise meta-analysis. All possible options of rma function in metafor package is available (default: the Sidik-Jonkman method (SJ)).
test	Method of the statistical inference for pairwise meta-analysis. All possible options of rma function in metafor package is available (default: the Hartung-Knapp adjustment (knha)).

Value

The results of the meta-analyses for all possible treatment pairs are provided.

• coding: A table that presents the correspondence between the numerical code and treatment categories (the reference category is coded as 1).

• measure: Outcome measure.

• method: Estimation method.

• test: Inference method.

• Summary effect measures: N (number of studies), summary estimates, 95% confidence intervals, and P-values for all possible pairs.

• Heterogeneity measures: N (number of studies), tau2 (heterogeneity variance) estimate, I2-statistic, and H2-statistic.

• Egger test: N (number of studies), P-value of the Egger test for assessing publication bias.

References

DerSimonian, R., and Laird, N. M. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials 7, 177-188.

Egger, M., Davey Smith, G., Schneider, M., and Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629-634.

Higgins, J. P. T., and Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine 21, 1539-1558.

IntHout, J., Ioannidis, J. P. A., and Borm, G. F. (2014). The Hartung–Knapp–Sidik–Jonkman method for random effects meta‐analysis is straightforward and considerably outperforms the standard DerSimonian–Laird method. BMC Medical Research Methodology 14, 25.

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36, Issue 3.

Examples

data(heartfailure)

hf2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="Placebo",data=heartfailure)

pairwise(hf2,method="REML",test="z")

NMA documentation built on Nov. 5, 2025, 7:15 p.m.