OBJECTIVES: In health technology assessment (HTA) decisions about reimbursement of new health technologies are largely based on effectiveness estimates obtained from pre-prepared meta-analysis of evidence from randomised controlled trials. However, there is not always a consensus amongst the decision-makers about the inclusion criteria of studies into the meta-analysis. Therefore an approach that allows stakeholders to manipulate the content of the meta-analysis, thus facilitating a critical sensitivity analysis in real time during the decision-making process, would be valuable from the point of view of the transparency of the HTA submissions. A Graphical-User-Interface (GUI) was designed to facilitate such a transparent decision-making process.   METHODS: The GUI was designed using freely available software packages which included WinBUGS for development of meta-analysis and meta-regression models and R which was used to design GUI, to link data with statistical models in WinBUGS and to extract the results.  R was also used to develop graphical tools for presentation of results (forest and bubble plots) and for visual assessment of publication bias (funnel plots). Software was designed for an illustrative example in rheumatoid arthritis where effectiveness of TNF-alpha inhibitors was measured on different scales (DAS-28, HAQ, ACR, and EULAR). RESULTS: R-based Transparent Interactive Decision Interrogator (R-TIDI) was developed, which is a user-friendly tool with “point and click” options that allows users to choose an outcome measure and run random-effects or fixed-effects meta-analysis and meta-regression models. Users are not required to have knowledge of statistical software or programming skills since the use of WinBUGS and R is entirely “behind the scenes”. R-TIDI enables users to interactively include/exclude studies from the meta-analysis allowing for conducting sensitivity analyses in real time.  CONCLUSIONS: R-TIDI is a useful tool for non-statistical decision-makers. It allows users to run sensitivity meta-analyses in a user-friendly environment during a decision-making setting avoiding disadvantages of pre-prepared analyses.