Cost-utility analyses of targeted immunomodulators in rheumatoid arthritis: systematic review

Abstract

Aims: Cost-utility (CU) modeling is a common technique used to determine whether new treatments represent good value for money. As with any modeling exercise, findings are a direct result of methodology choices, which may vary widely. Several targeted immuno-modulators have been launched in recent years to treat moderate-to-severe rheumatoid arthritis (RA) which have been evaluated using CU methods. Our objectives were to identify common and innovative modeling choices in moderate-to-severe RA and to highlight their implications for future models in RA. Materials and methods: A systematic literature search was conducted to identify CU models in moderate-to-severe RA published from January 2013 to June 2019. Studies must have included an active comparator and used quality-adjusted life-years (QALYs) as the common measure of effectiveness. Modeling methods were characterized by stakeholder perspective, simulation type, mapping between parameters, and data sources. Results: Thirty-one published modeling studies were reviewed spanning 13 countries and 9 drugs, with common methodological choices and innovations observed among them. Over the evaluated time period, we observed common methods and assumptions that are becoming more prominent in the RA CU modeling landscape, including patient-level simulations, two-stage models combining trial results and real-world evidence, real-world treatment durations, long-term health consequences, and Health Assessment Questionnaire (HAQ)-related hospitalization costs. Models that consider the societal perspective are increasingly being developed as well. Limitations: This review did not consider studies that did not report QALYs as a utility measure, models published only as conference abstracts, or cost-consequence models that did not report an incremental CU ratio. Conclusions: CU modeling for RA increasingly reflects real-world conditions and patient experiences which are anticipated to provide better information in the assessment of health technologies. Future CU models in RA should consider applying the observed advances in modeling choices to optimize their CU predictions and simulation of real-world outcomes.