Literature DB >> 17501939

Matching in studies of classification accuracy: implications for analysis, efficiency, and assessment of incremental value.

Holly Janes1, Margaret S Pepe.   

Abstract

In case-control studies evaluating the classification accuracy of a marker, controls are often matched to cases with respect to factors associated with the marker and disease status. In contrast with matching in epidemiologic etiology studies, matching in the classification setting has not been rigorously studied. In this article, we consider the implications of matching in terms of the choice of statistical analysis, efficiency, and assessment of the incremental value of the marker over the matching covariates. We find that adjustment for the matching covariates is essential, as unadjusted summaries of classification accuracy can be biased. In many settings, matching is the most efficient covariate-dependent sampling scheme, and we provide an expression for the optimal matching ratio. However, we also show that matching greatly complicates estimation of the incremental value of the marker. We recommend that matching be carefully considered in the context of these findings.

Mesh:

Year:  2007        PMID: 17501939     DOI: 10.1111/j.1541-0420.2007.00823.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  26 in total

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2.  Biomarker evaluation and comparison using the controls as a reference population.

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3.  Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve.

Authors:  Holly Janes; Margaret S Pepe
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Review 4.  Prediction models for risk classification in cardiovascular disease.

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5.  Biases introduced by choosing controls to match risk factors of cases in biomarker research.

Authors:  Margaret Sullivan Pepe; Jing Fan; Christopher W Seymour; Christopher Li; Ying Huang; Ziding Feng
Journal:  Clin Chem       Date:  2012-06-22       Impact factor: 8.327

6.  Estimating the receiver operating characteristic curve in matched case control studies.

Authors:  Hui Xu; Jing Qian; Nina P Paynter; Xuehong Zhang; Brian W Whitcomb; Shelley S Tworoger; Kathryn M Rexrode; Susan E Hankinson; Raji Balasubramanian
Journal:  Stat Med       Date:  2018-11-22       Impact factor: 2.373

7.  Testing for improvement in prediction model performance.

Authors:  Margaret Sullivan Pepe; Kathleen F Kerr; Gary Longton; Zheyu Wang
Journal:  Stat Med       Date:  2013-01-07       Impact factor: 2.373

8.  Estimating improvement in prediction with matched case-control designs.

Authors:  Aasthaa Bansal; Margaret Sullivan Pepe
Journal:  Lifetime Data Anal       Date:  2013-01-29       Impact factor: 1.588

9.  A parametric ROC model-based approach for evaluating the predictiveness of continuous markers in case-control studies.

Authors:  Y Huang; M S Pepe
Journal:  Biometrics       Date:  2009-12       Impact factor: 2.571

10.  Estimating the receiver operating characteristic curve in studies that match controls to cases on covariates.

Authors:  Margaret Sullivan Pepe; Jing Fan; Christopher W Seymour
Journal:  Acad Radiol       Date:  2013-04-17       Impact factor: 3.173

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