There are now several different approaches to perform a network meta-analysis or mixed treatment comparison.
The most commonly used and accessible ones are probably the following:
in a Bayesian framework:
- design-by-treatment interaction approach in WinBUGS (eg Jackson et al);
- hierarchical arm-based Bayesian modeling in WinBUGS (eg Zhao et al);
- hierarchical contrast-based (i.e. node-splitting) Bayesian modeling, either with WinBUGS or through
rjagsin R (eg Dias et al or van Valkenhoef et al);
- integrated nested Laplace approximations (INLA) in WinBUGS (eg Sauter et al);
in a frequentist framework:
- factorial analysis-of-variance in SAS (eg Piepho);
- multilevel network meta-analysis in SAS (eg Greco et al);
- multivariate meta-regression with
mvmetain Stata or R (eg White et al);
- network meta-analysis with
netmetain R (eg Lumley, which is however limited to two-arm trials, or Rucker et al).
My question is, simply: are they roughly equivalent or is there one which is preferable in most cases for the primary analysis (thus reserving the others for ancillary ones)?
Over the time, there have been some comparative analyses on methods for network meta-analysis: