OBJECTIVES: While several types of software are available, WinBUGS is the preferred statistical package to conduct network meta-analysis (NMA). Despite being widely used in the pharmaceutical industry, SAS use in NMA is limited. This research aims to compare results from meta-analyses conducted in WinBUGS and SAS.
METHODS: NMA of trial data were conducted in WinBUGS and SAS and their results compared. Networks of evidence with different levels of complexity, in terms of network structure, and the number of treatments and studies, were considered. Fixed (FE) and random effects (RE) analyses were conducted.
RESULTS: Analyses of a head-to-head network (TSD2 2011, Beta-blockers example: 2 treatments, 22 studies) showed strong consistency between SAS and WinBUGS for FE (log-OR [95%CI]: 0.2614 [0.3582;0.1623] vs 0.2622 [0.36;0.1637] respectively for SAS and WinBUGS) and RE analyses (0.2488 [0.3746;0.1168] vs 0.25 [0.759;0.1164] respectively). A closed-loop network (Jones et al. 2011, Cirrhosis example: 3 treatments, 26 studies) showed more consistency with FE than with RE. Observed differences between the means ranged from 0.0001 to 0.006 for the FE model, and from 0.01 to 0.16 for the RE model. All significant differences were consistent between WinBUGS and SAS results. Similar results were observed with a star-shaped network (TSD3 2011, Rheumatoid arthritis example: 7 treatments, 12 studies). SAS and WinBUGS produced consistent estimates of mean and standard deviation. However, in the RE analysis, credible intervals led to different conclusions. Etanercept was found to be significantly superior to methotrexate in WinBUGS but not in SAS (95% CI: [0.3717;7.391] vs [0.0165;7.0553]). The significant WinBUGS result was consistent with the head-to-head evidence.
CONCLUSIONS: SAS provides an alternative to WinBUGS to conduct NMA, and constitutes a potential means to validate WinBUGS results. However its results can significantly differ from WinBUGS depending on the nature of the network and so should be used with caution.
