OBJECTIVES: Anchored indirect treatment comparisons provide valid estimates of comparative effectiveness in the absence of head to head trials under the assumption of no imbalance in effect modifiers across trials. Effect modifiers can be any aspect of a trial that would lead to changes in relative effects including differences in patient characteristics. Best practices suggest the need for robust evidence of effect modifier status prior to adjustment. We provide an overview of a quantitative approach to the assessment of effect modification that leverages sparsity assumptions to decrease false positives.

METHODS: Stochastic search variable selection is used in combination with weakly informative priors to assess for the presence of effect modification under either a strong or data-based shared effect modifier assumption. Under the shared effect modifier assumption the relevant summary is the pooled average value of the effect modifier on the relevant linear predictor scale. Network meta-analysis methods for contrast data are leveraged for assessment of the presence of relationships that violate the shared effect modifier assumption. Approaches are validated via simulation.

RESULTS: Both approaches are appropriately calibrated under their respective data generating model and lead to superior inference compared to either testing from a single trial or conducting comparisons using vague priors.

CONCLUSIONS: Evaluation of effect modifiers in anchored ITCs in disease areas where subgroup effects are commonly reported can benefit from meta-analyses leveraging sparsity inducing priors. These priors seem particularly relevant in this application since it is commonly assumed that there are only a small number of true effect modifiers across therapies and can help reduce issues related to multiple testing of under powered estimates. Areas for future development include use of shrunken effects in subsequent adjustments and leveraging multivariate models in areas where there is substantial missing subgroup data.