Linked Life Data

20496347

Source:http://linkedlifedata.com/resource/pubmed/id/20496347

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-6-23
pubmed:abstractText
Multiple diagnostic tests and risk factors are commonly available for many diseases. This information can be either redundant or complimentary. Combining them may improve the diagnostic/predictive accuracy, but also unnecessarily increase complexity, risks, and/or costs. The improved accuracy gained by including additional variables can be evaluated by the increment of the area under (AUC) the receiver-operating characteristic curves with and without the new variable(s). In this study, we derive a new test statistic to accurately and efficiently determine the statistical significance of this incremental AUC under a multivariate normality assumption. Our test links AUC difference to a quadratic form of a standardized mean shift in a unit of the inverse covariance matrix through a properly linear transformation of all diagnostic variables. The distribution of the quadratic estimator is related to the multivariate Behrens-Fisher problem. We provide explicit mathematical solutions of the estimator and its approximate non-central F-distribution, type I error rate, and sample size formula. We use simulation studies to prove that our new test maintains prespecified type I error rates as well as reasonable statistical power under practical sample sizes. We use data from the Study of Osteoporotic Fractures as an application example to illustrate our method.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1521-4036
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
52
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
417-35
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Evaluating the improvement in diagnostic utility from adding new predictors.
pubmed:affiliation
Department of Radiology and Biomedical Imaging, University of California, San Francisco, 94143-0946, USA.
pubmed:publicationType
Journal Article, Evaluation Studies, Research Support, N.I.H., Extramural