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Network meta-analysis (NMA) usually provides estimates of the relativeeffects with the highest possible precision. However, sparse networks with fewavailable studies and limited direct evidence can arise, threatening therobustness and reliability of NMA estimates. In these cases, the limited amountof available information can hamper the formal evaluation of the underlying NMAassumptions of transitivity and consistency. In addition, NMA estimates fromsparse networks are expected to be imprecise and possibly biased as they relyon large sample approximations which are invalid in the absence of sufficientdata. We propose a Bayesian framework that allows sharing of informationbetween two networks that pertain to different population subgroups.Specifically, we use the results from a subgroup with a lot of direct evidence(a dense network) to construct informative priors for the relative effects inthe target subgroup (a sparse network). This is a two-stage approach where atthe first stage we extrapolate the results of the dense network to thoseexpected from the sparse network. This takes place by using a modifiedhierarchical NMA model where we add a location parameter that shifts thedistribution of the relative effects to make them applicable to the targetpopulation. At the second stage, these extrapolated results are used as priorinformation for the sparse network. We illustrate our approach through amotivating example of psychiatric patients. Our approach results in moreprecise and robust estimates of the relative effects and can adequately informclinical practice in presence of sparse networks.

Sharing information across patient subgroups to draw conclusions from sparse treatment networks