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Methodological Developments in Network Meta-Analysis and Comparative Effectiveness Research| Volume 25, ISSUE 1, SUPPLEMENT , S9, January 2022

P41 Crosnma: A New R Package to Synthesize Cross-Design Evidence and Cross-Format Data

      Objectives

      In network meta-analysis, we synthesize all relevant available evidence about health outcomes from competing treatments. That evidence might come from different study designs and in different formats: from non-randomized studies (NRS) or randomized controlled trials (RCT) as individual participant data (IPD) or as aggregate data (AD). To utilize all available evidence, we need a software that allows us to combine these different pieces of information accounting for their differences, e.g. RCTs have typically lower risk of bias than NRS.

      Methods

      We integrate the three-level hierarchical model that combine IPD and AD with the following four models that incorporate both RCT and NRS evidence by (a) ignoring their differences in risk of bias (b) using NRS to construct discounted treatment effect priors (c,d) adjusting for the risk of bias in each study and controlling the contribution of high risk of bias information in two different ways.

      Results

      We have implemented these models in a new R package, crosnma. This software allows us for conducting Bayesian network meta-analysis and meta-regression. Up to three study- or patient-level covariates can be also included, which may help explaining some of the heterogeneity and inconsistency across trials. The package runs a range of models with JAGS by generating the code automatically from user’s input.

      Conclusions

      crosnma is a new R package to conduct Bayesian network meta-analysis and meta-regression to synthesise cross-design evidence and cross-format data. We believe that this package will encourage the investigators to not discard any relevant evidence on their analysis. Authors are supported by the HTx-project. The HTx project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825162