The likelihood approach for the comparison of medical diagnostic system with multiple binary tests

Carol Y. Lin, Lance A. Waller and Robert H. Lyles

Journal of Applied Statistics, 2012, vol. 39, issue 7, 1437-1454

Abstract: Detection (diagnosis) techniques play an important role in clinical medicine. Early detection of diseases could be life-saving, and the consequences of false-positives and false-negatives could be costly. Using multiple measurements strategy is a popular tool to increase diagnostic accuracy. In addition to the new diagnostic technology, recent advances in genomics, proteomics, and other areas have allowed some of these newly developed individual biomarkers measured by non-invasive and inexpensive procedures (e.g. samples from serum, urine or stool) to progress from basic discovery research to assay development. As more tests become commercially available, there is an increasing interest for clinicians to request combinations of various non-invasive and inexpensive tests to increase diagnostic accuracy. Using information regarding individual test sensitivities and specificities, we proposed a likelihood approach to combine individual test results and to approximate or estimate the combined sensitivities and specificities of various tests taking into account the conditional correlations to quantify system performance. To illustrate this approach, we considered an example using various combinations of diagnostic tests to detect bladder cancer.

Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2011.650688 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:39:y:2012:i:7:p:1437-1454

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2011.650688

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().