A NOVEL FRAMEWORK FOR OPTIMISING THE VALUE OF PRECISION MEDICINE TECHNOLGIES

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.