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Adjusting for covariate effects in b...

Janes, Holly.

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Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve./
作者:	Janes, Holly.
面頁冊數:	179 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3780.
Contained By:	Dissertation Abstracts International66-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3183371
ISBN:	9780542246210

Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve.
Janes, Holly.

Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve. - 179 p.

Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3780.

Thesis (Ph.D.)--University of Washington, 2005.

Recent scientific and technological innovations have produced an explosion of potential biomarkers which are being investigated for their use in disease screening and diagnosis. In evaluating these new markers, it is often necessary to account for covariates which are associated with the biomarker of interest. For example, age is strongly associated with prostate-specific antigen (PSA), a biomarker for prostate cancer, and the discriminatory accuracy of PSA may also vary with age. In this thesis, we propose the subject-specific threshold ROC (SST-ROC), a novel summary of the covariate-adjusted diagnostic accuracy of the biomarker. We also investigate proper design of biomarker case-control studies in the presence of covariate effects.

ISBN: 9780542246210Subjects--Topical Terms:

517247
Statistics.

Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve.
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245 1 0 $a Adjusting for covariate effects in biomarker studies using the subject-specific threshold ROC curve.
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500 $a Source: Dissertation Abstracts International, Volume: 66-07, Section: B, page: 3780.
500 $a Chair: Margaret S. Pepe.
502 $a Thesis (Ph.D.)--University of Washington, 2005.
520 $a Recent scientific and technological innovations have produced an explosion of potential biomarkers which are being investigated for their use in disease screening and diagnosis. In evaluating these new markers, it is often necessary to account for covariates which are associated with the biomarker of interest. For example, age is strongly associated with prostate-specific antigen (PSA), a biomarker for prostate cancer, and the discriminatory accuracy of PSA may also vary with age. In this thesis, we propose the subject-specific threshold ROC (SST-ROC), a novel summary of the covariate-adjusted diagnostic accuracy of the biomarker. We also investigate proper design of biomarker case-control studies in the presence of covariate effects.
520 $a We motivate consideration of the SST-ROC by identifying the drawbacks of the traditional pooled and covariate-specific ROC curves in the presence of covariate effects. The SST-ROC curve is defined as the ROC curve for a rule which uses covariate-specific thresholds to define "test-positive". It can also be viewed as a weighted average of the covariate-specific sensitivities, holding the covariate-specific specificities constant. We illustrate the use of this new ROC curve using PSA data from the prostate cancer screening setting. Non-parametric and semi-parametric estimators are developed, and asymptotic distribution theory is provided. Consistent variance estimates are also put forward. We evaluate the small sample performance of the asymptotic theory using simulations, and assess the use of bootstrap variance estimates.
520 $a Matching is a design strategy which is commonly used when there are known covariate effects on test accuracy. We explore the motivations for and implications of matching, and then broaden our view to other types of control sampling strategies. We use the asymptotic distribution theory for the SST-ROC to explore the efficiency of matching and to develop recommendations more generally with regard to efficient study design.
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