OBJECTIVES: Network meta-analyses (NMAs) are commonly used to pool evidence from randomised controlled trials. Either a fixed effect or a random effect model can be used for analysis. Fixed effect models are often used when there are too few studies with which to estimate the between-study heterogeneity. Random effect models allow for between-study heterogeneity; however, in NMAs with few studies, uncertainty around point estimates are imprecisely estimated. Bayesian NMAs (BNMAs) using a random effect model and incorporating an informative prior for the between-study heterogeneity may offer a good solution. There is a need to understand how available methodologies, including those by Turner and colleagues, were being applied and on what basis the prior distributions were determined. Therefore, for this work, we aimed to review published BNMAs where informative priors were used for the between-study heterogeneity. METHODS: A pragmatic literature review was conducted using PubMed to include BNMAs, published in the last 10 years, where informative priors were used for the between-study heterogeneity. RESULTS: Twenty-two BNMAs were included. The number of studies included in individual BNMAs ranged from 4 to 96 studies. The rationale for using a prior distribution for the between-study heterogeneity was not stated in most studies. Among studies justifying the approach, informative priors were employed in order to reduce imprecision and improve the estimation of treatment differences. Most studies applied the methodologies proposed by Turner and colleagues. In one study, the choice of prior distribution was based on comparing models of selected prior distributions using the deviance information criterion (DIC). CONCLUSIONS: A consistent approach regarding the use of prior distribution to inform between-study heterogeneity was used in the literature. Nevertheless, more advances in prior elicitation methodologies are likely to increase the use of informative priors in future BNMAs.