Modelling the Cost‐Effectiveness of Statin Therapy in Rheumatoid Arthritis: A Markov‐Cycle Evaluation from National Data Bank for Rheumatic Diseases

Objectives Rheumatoid arthritis (RA) affects approximately 1% of the world\u27s population. In the United States, an estimated 1.5 million adults live with RA. Due to the high prevalence of comorbidities among patients with RA, the economic burden of the disease has raised concerns over the past decades. Researchers have increasingly recognized that the treatment strategy should focus on both disease progression and comorbidities management. The study aimed to evaluate the potential long‐term benefits of adjunctive statin therapy in combination with one or more biologic disease‐modifying antirheumatic drugs (DMARDs), versus biologic DMARDs alone, in patients with RA using a decision analytical framework and determined the associated incremental cost‐effectiveness ratios. Methods This study was conducted to determine the cost‐effectiveness of using adjunctive statin therapy in combination with one or more biologic DMARD, versus biologic DMARDs alone, in patients with RA. The study first conducted a secondary data analysis using the National Data Bank between 2003 and 2013 to identify study subjects and parameters used for further decision analysis. Second, a Markov simulation model was developed to assess the long‐term cost‐effectiveness of adjunctive statin therapy. Key findings The findings indicate that the clinical benefits of adjunctive statin therapy in treating RA disease progression resulted in an incremental cost per QALY gain, $36 642/QALY over 10 years, compared to biologic DMARDs alone. Conclusions Given the high costs associated with the management of RA, our study successfully developed a Markov decision model to simulate real‐world treatment processes and provide a fundamental framework for further investigating cost‐effeteness of statin therapy in RA. More importantly, it provides decision‐makers with scientific evidence of clinical and economic evaluation for optimal therapeutic outcomes