| Rheumatoid arthritis (RA) is a chronic debilitating disease characterized by progressive joint damage, reduced quality of life, loss of productivity and premature death. It affects 1% of the adult US population, and is one of the most demanding diseases on our healthcare resources. Biologic disease modifiers are new drugs that provide hope to improve the course of RA; however, biologics are among the most expensive specialty drugs. Although the treatment costs of RA have recently increased with the introduction of biologics, most of the economic and societal impacts are due to consequences of RA rather than direct treatment costs. Thus, the cost-effectiveness of biologics in RA is of high priority as recognized by many agencies including the National Institute of Health.;This thesis focuses on three limitations of the current cost-effectiveness analyses (CEA) of biologics in RA. First, Most CEAs are based on randomized clinical trials (RCT) that are rarely applicable to real-life clinical practice. This thesis examines the long-term comparative clinical- and cost-effectiveness of biologics using clinical practice data from a large registry of RA patients (The National Data-Bank of Rheumatic Diseases). Second, we lack a meta-analytical approach specific to CEAs, and previous tools are deemed inappropriate. This thesis presents a novel approach of meta-analysis specific to CEAs. Using this tool we examine if prior CEAs of biologics in RA are consistent. Third, due to the biologics' high costs, RA treatment guidelines often recommend biologics as second line agents after nonbiologics. However, early aggressive treatment is crucial to avoid permanent joint damage. In this thesis we use Markov decision processes (MDP) as an innovative approach to identify the optimal timing of biologics in RA.;The results from this analysis have significant policy, clinical and methodological implications. This work provides important insights into the comparative effectiveness of biologics in RA from a US societal perspective, which can influence health policy and medical insurance coverage decisions. Methodologically, the proposed meta-analytical approach can be applied to other conditions, and have the potential to reconcile the inconsistencies in published CEAs and improve the quality of future studies. |
