OBJECTIVES: Given biological therapies in psoriatic arthritis (PsA) treatment paradigm are expensive, cost-effectiveness evaluations can be a valuable tool in payer healthcare decision making. We sought to review the economic evidence and cost-effectiveness of all available biologics developed for treatment of PsA. METHODS: We conducted a structured literature search of published and unpublished literature from year 1996 to 2012. We included modeling and other economic studies that assessed cost-effectiveness of biologics and excluded studies that evaluated therapies other than biologics. RESULTS: A total of 9 studies involving moderate to severe active PsA patients were analyzed. Most of the cost-effectiveness studies were conducted in the United Kingdom (N=6) using direct payer perspective. As no head-to-head trials between biological therapies were present, either indirect comparison with Bayesian technique or network meta-analyses were used to synthesize evidence. Treatment clinical effectiveness was measured by psoriatic arthritis response criteria (PsARC) and/or psoriasis area and severity index (PASI). Functional status was measured by health assessment questionnaire (HAQ). Decision analytical model with underlying Markov modeling was considered by majority of the studies to build the cost-effectiveness model using cohort of patients, while few studies used patient level simulations. Disease-modifying anti-rheumatic drugs (DMARDs) were primarily considered as comparators. Time horizon varied from 10 years to lifetime. All studies employed quality adjusted life years (QALYs) as their measure of effectiveness. Costs and QALYs discounting rate varied from 3.5 to 6% and 1.5 to 3.5% respectively. Incremental cost-effectiveness ratio per QALY varied from £17,000 to £40,000. CONCLUSIONS: Although biologics are considered expensive, they improve patient’s quality of life in the long-run. Existing cost-effectiveness studies have differences in their assumptions and methodologies, and provide valuable inputs towards building the set of disease related parameters. Next generation of biologic therapies in the near future can benefit from these analyses.