Tuesday, October 20, 2015: 2:30 PM
Grand Ballroom C (Hyatt Regency St. Louis at the Arch)

Eric Jutkowitz1, Fernando Alarid-Escudero, MS2, Hyon Choi, M.D., Dr.P.H.3, Karen M. Kuntz, ScD2 and Hawre Jalal, PhD4, (1)University of Minnesota School of Public Health, Minneapolis, MN, (2)University of Minnesota, Minneapolis, MN, (3)Department of Medicine, Massachusetts General Hospital, Boston,, MA, (4)Department of Health Policy and Management, University of Pittsburgh, Graduate School of Public Health, Pittsburgh, PA

Purpose: Gout is the most common inflammatory arthritis in the United States, and several urate-lowering treatment strategies are used to manage symptoms. The value of collecting additional information of key parameters in the cost-effectiveness of urate-lowering treatment strategies for the management of gout is unknown.  We apply a meta-modeling approach to calculate the expected value of perfect information (EVPI), expected value of partial perfect information (EVPPI), and the expected value of sample information for parameters (EVPSI) on all model parameters (e.g., utilities, efficacy, and cost). 

Methods: We used a previously developed model that evaluated the cost-effectiveness of five urate-lowering strategies: no treatment, allopurinol or febuxostat only, allopurinol- febuxostat sequential therapy, and febuxostat-allopurinol sequential therapy. Health states in the model accounted for disease status: controlled, uncontrolled on medication, and uncontrolled off medication. To quantify uncertainty in the model we conducted a probabilistic sensitivity analysis (PSA). We implemented a linear regression meta-model to the dataset generated from the PSA. Conceptually similar parameters were evaluated together (e.g., utilities) since a single study is likely to inform all of these parameters. To inform future research design we extrapolated EVPI, EVPPI, and EVPSI on a United States population level for an annual incidence of 29,376 new gout patients assuming a decision lifetime of 10 years.  Finally, we calculated the optimal sample size of a future study assuming a patient survey would be administered during a clinical visit (fixed cost $6,000; cost per patient $100) to evaluate the parameter group of interest.

Results: Population EVPI varies by a decision maker's willingness-to-pay (WTP) per quality-adjusted life year and is $227 million for WTP of $100,000. EVPPI is highest for utility parameters when WTP is $50,000-$100,000. Figure 1 shows population EVPSI for parameters evaluating utilities, cost of research, expected net benefit of sampling (ENBS), and the optimal sample size for a survey conducted in a clinic evaluating gout patients' health utilities. Given a WTP of $100,000, the optimal sample size of a survey based research study evaluating the health utility of gout patients is 8,600. If the costs of research doubles the optimal sample size is 5,700.

Conclusions:  Future studies should be conducted to evaluate utility of gout patients.