OP51 Use Of Real-World Data In Cost-effectiveness Analysis Of Sequential Biologic Treatment For Rheumatoid Arthritis

Abstract

IntroductionIn health technology assessment (HTA), economic evaluations assessing biologic drugs for rheumatoid arthritis (RA) involve modeling patients’ responses to multiple treatments given sequentially over a lifetime horizon. When data from randomized controlled trials (RCTs) are scarce, data from non-randomized studies (e.g., single-arm trials [SATs] and disease registries) can be used to supplement the evidence base. This research aimed to demonstrate meta-analytic methods for combining effectiveness data from randomized and non-randomized studies and their corresponding impact on cost-effectiveness estimates.MethodsData comparing patients receiving second-line rituximab with continued background non-biologic treatment were extracted from one RCT and six SATs identified in an HTA assessing second-line rituximab for RA, and from the British Society for Rheumatology Biologics Register-Rheumatoid Arthritis, by applying a target trial emulation approach. A binomial meta-analysis model was used to estimate the probabilities of achieving the European League against Rheumatism (EULAR) response criteria by pooling data from the RCT, SATs, and the registry. The probabilities were entered into a decision model from a previous HTA to derive incremental cost-effectiveness ratio (ICER) estimates for treatment strategies with and without biologic drugs.ResultsCompared with the original analysis, the estimated probability of at least a moderate EULAR response on rituximab from combined sources was substantially lower. For example, the probability obtained from an RCT was 0.68 (95% credible interval [CrI]: 0.345, 0.907), but only 0.29 (95% [CrI]: 0.242, 0.333) when using RCT plus registry data and 0.29 (95% CrI: 0.244, 0.336) for combined RCT, registry, and SAT data. In the cost-effectiveness analysis, the median ICERs were higher when including real-world data.ConclusionsSynthesis of all relevant data, including RWD, provides additional information regarding the variability in cost-effectiveness estimates and can be considered in sensitivity analyses for HTA decision-making.