Economic optimal operating point for genetic testing

Economic evaluations of diagnostic tests attempt to elicit the downstream effects the test has on treatment and its costs and benefits. In these studies, test accuracy plays a central role since eligibility of treatment is conditional on the test result. Once we are able to explore the true positive and false positive rates in the receiver operating curve (ROC) space, we can implement this into a decision model and derive an optimal operating point for the test that gives the ideal treatment allocation within the patient group at the payer's level of willingness to pay in the cost-effectiveness space. The ROTS approach (expansion curves from ROC-space to cost-effectiveness space) is one  way to solve this, but has never been applied to genetic testing previously. The project aim is to expand on a previous analysis of genetic testing for breast - and ovarian cancer by evaluating alternative risk thresholds as a cut-off for positivity, therein investigating if the prevously clinically derived thresholdis deviates from the economic optimal operating point.