OBJECTIVES: In health technology assessment (HTA), cost-effectiveness analyses assessing biologic drugs for rheumatoid arthritis (RA) involve modelling patient response to a sequence of treatments over their lifetime. Where data from randomised controlled trials (RCTs) are limited, non-randomised sources (e.g., single-arm trials (SATs), disease registries) can provide supplemental evidence. This research aims to demonstrate meta-analysis methods for combining effectiveness data from randomised and non-randomised sources and their impact on cost-effectiveness estimates.

METHODS: Data were extracted for one RCT and six SATs identified in a HTA assessing 2^nd-line rituximab as treatment for RA (Malottki et al, 2011), and from the British Society for Rheumatology Biologics Register – for Rheumatoid Arthritis comparing patients receiving 2^nd-line rituximab versus non-biologic drugs. A Binomial meta-analysis model was fit 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 inputted into a decision model from a previous HTA (Stevenson et al, 2016) resulting in incremental cost-effectiveness ratio (ICER) estimates comparing treatment strategies with and without biologic drugs.

RESULTS: Compared to the original analysis, the probability of at least a moderate EULAR response on rituximab estimated from combined sources was substantially lower. For example, the probability obtained from an RCT was 0.690 (95% CrI: 0.358, 0.913), but only 0.301 (0.262, 0.342) when using RCT+registry or 0.299 (0.260, 0.340) from RCT+registry+SATs. In the cost-effectiveness analysis, however, the median ICERs were comparable.

CONCLUSIONS: Synthesis of all relevant data provides additional information regarding variability in cost-effectiveness estimates, and can be considered in sensitivity analyses for HTA decision-making.