Integrating randomized controlled trials and non-randomized studies of interventions to assess the effect of rare events: a Bayesian re-analysis of two meta-analyses

BackgroundThere is a growing trend to include non-randomised studies of interventions (NRSIs) in rare events meta-analyses of randomised controlled trials (RCTs) to complement the evidence from the latter. An important consideration when combining RCTs and NRSIs is how to address potential bias and down-weighting of NRSIs in the pooled estimates. The aim of this study is to explore the use of a power prior approach in a Bayesian framework for integrating RCTs and NRSIs to assess the effect of rare events.MethodsWe proposed a method of specifying the down-weighting factor based on judgments of the relative magnitude (no information, and low, moderate, serious and critical risk of bias) of the overall risk of bias for each NRSI using the ROBINS-I tool. The methods were illustrated using two meta-analyses, with particular interest in the risk of diabetic ketoacidosis (DKA) in patients using sodium/glucose cotransporter-2 (SGLT-2) inhibitors compared with active comparators, and the association between low-dose methotrexate exposure and melanoma.ResultsNo significant results were observed for these two analyses when the data from RCTs only were pooled (risk of DKA: OR = 0.82, 95% confidence interval (CI): 0.25-2.69; risk of melanoma: OR = 1.94, 95%CI: 0.72-5.27). When RCTs and NRSIs were directly combined without distinction in the same meta-analysis, both meta-analyses showed significant results (risk of DKA: OR = 1.50, 95%CI: 1.11-2.03; risk of melanoma: OR = 1.16, 95%CI: 1.08-1.24). Using Bayesian analysis to account for NRSI bias, there was a 90% probability of an increased risk of DKA in users receiving SGLT-2 inhibitors and an 91% probability of an increased risk of melanoma in patients using low-dose methotrexate.ConclusionsOur study showed that including NRSIs in a meta-analysis of RCTs for rare events could increase the certainty and comprehensiveness of the evidence. The estimates obtained from NRSIs are generally considered to be biased, and the possible influence of NRSIs on the certainty of the combined evidence needs to be carefully investigated.