Monday, June 13, 2016: 14:45
Stephenson Room, 5th Floor (30 Euston Square)

Philip Akude, MSc1, Reza Mahjoub, PhD2, Mike Paulden, MA., MSc.1, Chase Hollman1 and Christopher McCabe, PhD3, (1)University of Alberta, Edmonton, AB, Canada, (2)Department of Emergency Medicine, University of Alberta, EDmonton, AB, Canada, (3)Department of Emergency Medicine, University of Alberta, Edmonton, AB, Canada
Purpose: Develop methods for combining evidence on the test(s) and treatment components of co-dependent technologies that identify the cost effective cut-points on the test components for pre-specified values of the Willingness to Pay for Health.

Method(s): We propose a framework for describing co-dependent technologies that consists of three tests (genotypic diagnosis, phenotypic expression and therapy responder status) and a treatment. Based upon the presence of the condition of interest, the second and third tests characterize the ability to respond to therapy and the phenotypic expression – which places a limit on the ability to benefit from therapy – respectively. Three decision variables are identified – the cut-point for the probability of responding to therapy, the cut-point for the phenotypic expression that leads to treatment and the willingness to pay for health gain. The effectiveness of the therapy in responders and non-responders is determined exogenously.

Result(s): Our analysis shows that for a given probability of response, the optimal cut-point for the phenotypic expression is identified as the point at which the benefits for a responding patient means the patient is indifferent between the new treatment and standard care.  We present a series of analyses exploring the relationship between the distributions of the probability of responding to therapy, phenotypic expression and the net benefit from the new technology.

Conclusion(s): Our analyses demonstrate that the benefit from the adoption of precision medicine technologies can be optimized by treating response probability and phenotypic expression as decision variables not exogenously determined parameters.