Literature DB >> 22875756

Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context.

Kathleen F Kerr1, Aasthaa Bansal, Margaret S Pepe.   

Abstract

In this issue of the Journal, Pencina and et al. (Am J Epidemiol. 2012;176(6):492-494) examine the operating characteristics of measures of incremental value. Their goal is to provide benchmarks for the measures that can help identify the most promising markers among multiple candidates. They consider a setting in which new predictors are conditionally independent of established predictors. In the present article, the authors consider more general settings. Their results indicate that some of the conclusions made by Pencina et al. are limited to the specific scenarios the authors considered. For example, Pencina et al. observed that continuous net reclassification improvement was invariant to the strength of the baseline model, but the authors of the present study show this invariance does not hold generally. Further, they disagree with the suggestion that such invariance would be desirable for a measure of incremental value. They also do not see evidence to support the claim that the measures provide complementary information. In addition, they show that correlation with baseline predictors can lead to much bigger gains in performance than the conditional independence scenario studied by Pencina et al. Finally, the authors note that the motivation of providing benchmarks actually reinforces previous observations that the problem with these measures is they do not have useful clinical interpretations. If they did, researchers could use the measures directly and benchmarks would not be needed.

Mesh:

Substances:

Year:  2012        PMID: 22875756      PMCID: PMC3530353          DOI: 10.1093/aje/kws210

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  10 in total

1.  Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve.

Authors:  Raluca Mihaescu; Moniek van Zitteren; Mandy van Hoek; Eric J G Sijbrands; André G Uitterlinden; Jacqueline C M Witteman; Albert Hofman; M G Myriam Hunink; Cornelia M van Duijn; A Cecile J W Janssens
Journal:  Am J Epidemiol       Date:  2010-06-18       Impact factor: 4.897

2.  On criteria for evaluating models of absolute risk.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  Biostatistics       Date:  2005-04       Impact factor: 5.899

3.  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

4.  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

5.  Use of reclassification for assessment of improved prediction: an empirical evaluation.

Authors:  Ioanna Tzoulaki; George Liberopoulos; John P A Ioannidis
Journal:  Int J Epidemiol       Date:  2011-02-16       Impact factor: 7.196

6.  Commentary: Reporting standards are needed for evaluations of risk reclassification.

Authors:  Margaret S Pepe; Holly Janes
Journal:  Int J Epidemiol       Date:  2011-05-13       Impact factor: 7.196

7.  Interpreting incremental value of markers added to risk prediction models.

Authors:  Michael J Pencina; Ralph B D'Agostino; Karol M Pencina; A Cecile J W Janssens; Philip Greenland
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

8.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ewout W Steyerberg
Journal:  Stat Med       Date:  2010-11-05       Impact factor: 2.373

9.  Joint modeling, covariate adjustment, and interaction: contrasting notions in risk prediction models and risk prediction performance.

Authors:  Kathleen F Kerr; Margaret S Pepe
Journal:  Epidemiology       Date:  2011-11       Impact factor: 4.822

10.  When does combining markers improve classification performance and what are implications for practice?

Authors:  Aasthaa Bansal; Margaret Sullivan Pepe
Journal:  Stat Med       Date:  2013-01-24       Impact factor: 2.373

  10 in total
  15 in total

1.  Interpretable Deep Models for ICU Outcome Prediction.

Authors:  Zhengping Che; Sanjay Purushotham; Robinder Khemani; Yan Liu
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

2.  Pencina et al. respond to "The incremental value of new markers" and "Clinically relevant measures? A note of caution".

Authors:  Michael J Pencina; Ralph B D'Agostino; Olga V Demler; A Cecile J W Janssens; Philip Greenland
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

3.  Clinical utility in evaluation of risk models.

Authors:  Sholom Wacholder
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

Review 4.  Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease.

Authors:  Chirag R Parikh; Heather Thiessen-Philbrook
Journal:  J Am Soc Nephrol       Date:  2014-05-01       Impact factor: 10.121

5.  First things first: risk model performance metrics should reflect the clinical application.

Authors:  Kathleen F Kerr; Holly Janes
Journal:  Stat Med       Date:  2017-12-10       Impact factor: 2.373

6.  Development and validation of a cardiovascular disease risk-prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT).

Authors:  Douglas G Manuel; Meltem Tuna; Carol Bennett; Deirdre Hennessy; Laura Rosella; Claudia Sanmartin; Jack V Tu; Richard Perez; Stacey Fisher; Monica Taljaard
Journal:  CMAJ       Date:  2018-07-23       Impact factor: 8.262

7.  Quantifying the value of biomarkers for predicting mortality.

Authors:  Noreen Goldman; Dana A Glei
Journal:  Ann Epidemiol       Date:  2015-08-29       Impact factor: 3.797

8.  Recent BRCAPRO upgrades significantly improve calibration.

Authors:  Emanuele Mazzola; Jonathan Chipman; Su-Chun Cheng; Giovanni Parmigiani
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2014-06-02       Impact factor: 4.254

9.  Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment.

Authors:  Jaya M Satagopan; Alexia Iasonos; Qin Zhou
Journal:  Genet Epidemiol       Date:  2015-09-09       Impact factor: 2.135

Review 10.  Net reclassification indices for evaluating risk prediction instruments: a critical review.

Authors:  Kathleen F Kerr; Zheyu Wang; Holly Janes; Robyn L McClelland; Bruce M Psaty; Margaret S Pepe
Journal:  Epidemiology       Date:  2014-01       Impact factor: 4.822

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