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The arrival of personalized medicine in the clinic means that treatment decisions will increasingly rely on test results. The challenge of limited healthcare resources means that the dissemination of these technologies will be dependent on their value in relation to their cost, i.e., their cost effectiveness. Phelps and Mushlin have described how to optimize tests to meet a cost-effectiveness target. However, when tests are applied repeatedly the case mix of the patients tested changes with each administration, and this impacts upon the value of each subsequent test administration. In this article, we present a modification of Phelps and Mushlin's framework for diagnostic tests; to identify the cost-effective cut-off for monitoring tests. Using the Ca125 test monitoring for relapse in ovarian cancer, we show how the repeated use of the initial cut-off can lead to a substantially increased false-negative rate compared with the monitoring cut-off-over 4% higher than in this example-with the associated harms for individual and population health.

Methods for Identifying the Cost-effective Case Definition Cut-Off for Sequential Monitoring Tests: An Extension of Phelps and Mushlin