| Title |
Methods for Identifying the Cost-effective Case Definition Cut-Off for Sequential Monitoring Tests: An Extension of Phelps and Mushlin
|
|---|---|
| Published in |
PharmacoEconomics, February 2014
|
| DOI | 10.1007/s40273-014-0134-1 |
| Pubmed ID | |
| Authors |
Roberta Longo, Paul Baxter, Peter Hall, Jenny Hewison, Mehran Afshar, Geoff Hall, Christopher McCabe |
| Abstract |
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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 6% |
| Canada | 2 | 6% |
| Unknown | 29 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 24% |
| Researcher | 7 | 21% |
| Student > Doctoral Student | 4 | 12% |
| Student > Bachelor | 3 | 9% |
| Student > Postgraduate | 3 | 9% |
| Other | 5 | 15% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 27% |
| Economics, Econometrics and Finance | 8 | 24% |
| Social Sciences | 4 | 12% |
| Nursing and Health Professions | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 February 2014.
All research outputs
#16,701,754
of 24,562,945 outputs
Outputs from PharmacoEconomics
#1,637
of 1,959 outputs
Outputs of similar age
#200,251
of 318,145 outputs
Outputs of similar age from PharmacoEconomics
#18
of 27 outputs
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