Document Detail


Compare diagnostic tests using transformation-invariant smoothed ROC curves().
MedLine Citation:
PMID:  22639484     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Receiver operating characteristic (ROC) curve, plotting true positive rates against false positive rates as threshold varies, is an important tool for evaluating biomarkers in diagnostic medicine studies. By definition, ROC curve is monotone increasing from 0 to 1 and is invariant to any monotone transformation of test results. And it is often a curve with certain level of smoothness when test results from the diseased and non-diseased subjects follow continuous distributions. Most existing ROC curve estimation methods do not guarantee all of these properties. One of the exceptions is Du and Tang (2009) which applies certain monotone spline regression procedure to empirical ROC estimates. However, their method does not consider the inherent correlations between empirical ROC estimates. This makes the derivation of the asymptotic properties very difficult. In this paper we propose a penalized weighted least square estimation method, which incorporates the covariance between empirical ROC estimates as a weight matrix. The resulting estimator satisfies all the aforementioned properties, and we show that it is also consistent. Then a resampling approach is used to extend our method for comparisons of two or more diagnostic tests. Our simulations show a significantly improved performance over the existing method, especially for steep ROC curves. We then apply the proposed method to a cancer diagnostic study that compares several newly developed diagnostic biomarkers to a traditional one.
Authors:
Liansheng Tang; Pang Du; Chengqing Wu
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Journal of statistical planning and inference     Volume:  140     ISSN:  0378-3758     ISO Abbreviation:  -     Publication Date:  2010 Nov 
Date Detail:
Created Date:  2012-5-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101250629     Medline TA:  J Stat Plan Inference     Country:  -    
Other Details:
Languages:  ENG     Pagination:  3540-3551     Citation Subset:  -    
Affiliation:
Department of Statistics, George Mason University, Fairfax, VA 22030, USA.
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