Literature DB >> 23807696

Evaluating subject-level incremental values of new markers for risk classification rule.

T Cai1, L Tian, D Lloyd-Jones, L J Wei.   

Abstract

Suppose that we need to classify a population of subjects into several well-defined ordered risk categories for disease prevention or management with their "baseline" risk factors/markers. In this article, we present a systematic approach to identify subjects using their conventional risk factors/markers who would benefit from a new set of risk markers for more accurate classification. Specifically for each subgroup of individuals with the same conventional risk estimate, we present inference procedures for the reclassification and the corresponding correct re-categorization rates with the new markers. We then apply these new tools to analyze the data from the Cardiovascular Health Study sponsored by the US National Heart, Lung, and Blood Institute. We used Framingham risk factors plus the information of baseline anti-hypertensive drug usage to identify adult American women who may benefit from the measurement of a new blood biomarker, CRP, for better risk classification in order to intensify prevention of coronary heart disease for the subsequent 10 years.

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Year:  2013        PMID: 23807696      PMCID: PMC4527584          DOI: 10.1007/s10985-013-9272-6

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  16 in total

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3.  Risk prediction and finding new independent prognostic factors.

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Journal:  J Hypertens       Date:  2006-04       Impact factor: 4.844

4.  The need for reorientation toward cost-effective prediction: comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond' by Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929).

Authors:  Yueh-Yun Chi; Xiao-Hua Zhou
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

5.  Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ralph B D'Agostino; Ramachandran S Vasan
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

6.  The effect of including C-reactive protein in cardiovascular risk prediction models for women.

Authors:  Nancy R Cook; Julie E Buring; Paul M Ridker
Journal:  Ann Intern Med       Date:  2006-07-04       Impact factor: 25.391

7.  Prediction of coronary heart disease using risk factor categories.

Authors:  P W Wilson; R B D'Agostino; D Levy; A M Belanger; H Silbershatz; W B Kannel
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8.  Multiple biomarkers for the prediction of first major cardiovascular events and death.

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Authors:  Nancy R Cook
Journal:  Circulation       Date:  2007-02-20       Impact factor: 29.690

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Authors:  Paul M Ridker; Julie E Buring; Nader Rifai; Nancy R Cook
Journal:  JAMA       Date:  2007-02-14       Impact factor: 56.272

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  4 in total

1.  This special issue contains several papers on clinical trials, exemplifying Ross Prentice's influence. Preface.

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Journal:  Lifetime Data Anal       Date:  2013-10-16       Impact factor: 1.588

2.  Estimation of treatment policies based on functional predictors.

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Journal:  Stat Sin       Date:  2014-07       Impact factor: 1.261

3.  On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.

Authors:  Hajime Uno; Tianxi Cai; Michael J Pencina; Ralph B D'Agostino; L J Wei
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4.  PERFORMANCE GUARANTEES FOR INDIVIDUALIZED TREATMENT RULES.

Authors:  Min Qian; Susan A Murphy
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