Assessing the impact of a matching adjusted indirect comparison in a bayesian network meta analysis

if IPD is available for some or all trials in an NMA, then incorporating this IPD into an NMA is routinely considered to be preferable. However, the situation often arises where a researcher has IPD for trials concerning a particular treatment (for example from a spon- sor), but none for other trials. Therefore, one can reweight the IPD so that the covariate characteristics in the IPD trials match that of the aggregate data (AgD) trials, using a Matching Adjusted Indirect Comparison (MAIC). We assess the impact of using the reweighted aggregated data, obtained by the MAIC, in a Bayesian NMA for a connected treatment network. We apply this method to a network of multiple myeloma treatments in newly diagnosed patients (ndMM), where the outcome is progression free survival. We investigate the reliability of the methods and results through a simulation study. The ndMM network con-sists of three IPD studies comparing lenalidomide to placebo (Len-Placebo), one AgD study comparing Len-Placebo, and one AgD study comparing thalidomide to placebo (Thal-Placebo). We therefore investigate two options of weighting the covariates: 1. all three studies are weighted separately to match the AgD Thal-Placebo trial. 2. patients are weighted across all three IPD studies to match the AgD Thal-Placebo trial, but the NMA considers each trial separately. We observe limited bene t to MAIC in the full network population. While MAIC can be beneficial as a sensitivity analysis to confirm results across patient populations, we advise that MAIC is used and interpreted with caution.