Methods for Identifying the Cost-effective Case Definition Cut-Off for Sequential Monitoring Tests: An Extension of Phelps and Mushlin
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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.
KeywordsOvarian Cancer Case Definition Receiver Operator Curve Sick Person Monitoring Test
This article presents independent research partially funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0707-10101: Evaluating the benefits for patients and the NHS of new and existing biological fluid biomarkers in liver and renal disease). Additional support was received through the Medical Technologies Innovation and Knowledge Centre funded by the UK Research Councils, EPSRC, and BBSRC, and the Technology Strategy Board. We wish to acknowledge helpful discussions with colleagues on the NIHR Programme Grant award, especially Doug Altman, Jon Deeks, Walter Gregory, and Peter Selby. Prof. McCabe’s research is made possible by the Capital Health Research Chair Endowment to the University of Alberta Faculty of Medicine and Dentistry. He also received funding from the Genome Canada Large Scale Applied Research Program on Personalized Medicine for some of this work. The work reported in this paper and any errors remain the responsibility of the authors.
All individuals who made substantive contributions to the ideas presented in this article are included as authors. The initial concepts for this article were proposed by Christopher McCabe. Roberta Longo and Paul Baxter undertook the modeling work to provide the example and prepared the draft for the manuscript. Peter Hall, Geoff Hall, and Mehran Afshar provided the clinical data and contributed to the manuscript writing. Christopher McCabe and Jenny Hewison supervised the preparation of the draft and contributed to the manuscript writing.
Conflicts of interest
Roberta Longo has no conflicts of interest.
Paul Baxter has no conflicts of interest.
Peter Hall has no conflicts of interest.
Jenny Hewison has no conflicts of interest.
Mehran Afshar has no conflicts of interest.
Geoff Hall has no conflicts of interest.
Christopher McCabe has no conflicts of interest.
The views and opinions expressed by the authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, the Department of Health, or Genome Canada.
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