Objectives: The promise of personalized medicine has been difficult to realize due to a number of barriers to its development, including uncertainty regarding clinical utility and economic value. We aimed to provide an estimation of the cost-effectiveness of the use of a pharmacogenetic diagnostic test for Cytochrome P450 (CYP450) in schizophrenia used to adjust dosing prior to risperidone initiation, relative to traditional dosing patterns of risperidone in UK. Methods: A deterministic decision model was developed to compare the health gain and costs of a patient stratification strategy versus traditional dosing. The time horizon of the model is two years, which consists of 24 cycles of one month. Input parameters were taken from the literature. Influential parameters were identified through sensitivity and scenario analyses. Results: The patient stratification strategy improved health compared to the traditional dosing scheme, at an additional cost of £2059/patient. Varying key parameters of the model showed that the results were most sensitive to the assumptions regarding costs and health utility of patients who experienced treatment failure and the accuracy of the test. The price of the drug had the least influence on the results. Conclusions: This study suggests that testing for CYP450 polymorphisms prior to treatment with risperidone to allow dose adjustment will most likely be cost-effective. Potential future research for the assessment of companion diagnostics and possible approaches to dealing with the challenging evidence generation in this growing field are discussed.

Additional Metadata
Keywords Cost effectiveness, Diagnostic tests, Economic evaluation, Pharmacogenetics, Schizophrenia, Stratified medicine
Persistent URL dx.doi.org/10.1016/j.hlpt.2014.08.004, hdl.handle.net/1765/101548
Journal Health Policy and Technology
Citation
Rejon-Parrilla, J.C. (Juan Carlos), Nuijten, M.J.C, Redekop, W.K, & Gaultney, J.G. (2014). Economic evaluation of the use of a pharmacogenetic diagnostic test in schizophrenia. Health Policy and Technology, 3(4), 314–324. doi:10.1016/j.hlpt.2014.08.004