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

Journal of Comparative Effectiveness Research, 2020


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.


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.


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.

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Swallow E, Patterson-Lomba O, Ayyagari R, Pelletier C, Mehta R, Signorovitch J