Causal inference and adjustment for reference-arm risk in indirect treatment comparison meta-analysis

Abstract

Aim: To illustrate that bias associated with indirect treatment comparison and network meta-analyses can be reduced by adjusting for outcomes on common reference arms. Materials & methods: Approaches to adjusting for reference-arm effects are presented within a causal inference framework. Bayesian and Frequentist approaches are applied to three real data examples. Results: Reference-arm adjustment can significantly impact estimated treatment differences, improve model fit and align indirectly estimated treatment effects with those observed in randomized trials. Reference-arm adjustment can possibly reverse the direction of estimated treatment effects. Conclusion: Accumulating theoretical and empirical evidence underscores the importance of adjusting for reference-arm outcomes in indirect treatment comparison and network meta-analyses to make full use of data and reduce the risk of bias in estimated treatments effects.