Literature DB >> 19574417

Improving the biomarker pipeline to develop and evaluate cancer screening tests.

Stuart G Baker1.   

Abstract

The biomarker pipeline to develop and evaluate cancer screening tests has three stages: identification of promising biomarkers for the early detection of cancer, initial evaluation of biomarkers for cancer screening, and definitive evaluation of biomarkers for cancer screening. Statistical and biological issues to improve this pipeline are discussed. Although various recommendations, such as identifying cases based on clinical symptoms, keeping biomarker tests simple, and adjusting for postscreening noise, have been made previously, they are not widely known. New recommendations include more frequent specimen collection to help identify promising biomarkers and the use of the paired availability design with interval cases (symptomatic cancers detected in the interval after screening) for initial evaluation of biomarkers for cancer screening.

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Year:  2009        PMID： 19574417      PMCID： PMC2728744          DOI： 10.1093/jnci/djp186

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN： 0027-8874            Impact factor:   13.506


  30 in total

Review 1.  Phases of biomarker development for early detection of cancer.

Authors:  M S Pepe; R Etzioni; Z Feng; J D Potter; M L Thompson; M Thornquist; M Winget; Y Yasui
Journal:  J Natl Cancer Inst       Date:  2001-07-18       Impact factor: 13.506

2.  Basic principles of ROC analysis.

Authors:  C E Metz
Journal:  Semin Nucl Med       Date:  1978-10       Impact factor: 4.446

3.  An early- and late-stage convolution model for disease natural history.

Authors:  Paul F Pinsky
Journal:  Biometrics       Date:  2004-03       Impact factor: 2.571

4.  Identifying combinations of cancer markers for further study as triggers of early intervention.

Authors:  S G Baker
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

5.  All-cause mortality in randomized trials of cancer screening.

Authors:  William C Black; David A Haggstrom; H Gilbert Welch
Journal:  J Natl Cancer Inst       Date:  2002-02-06       Impact factor: 13.506

6.  Estimating the cumulative risk of a false-positive test in a repeated screening program.

Authors:  Jian-Lun Xu; Richard M Fagerstrom; Philip C Prorok; Barnett S Kramer
Journal:  Biometrics       Date:  2004-09       Impact factor: 2.571

7.  Markers for early detection of cancer: statistical guidelines for nested case-control studies.

Authors:  Stuart G Baker; Barnett S Kramer; Sudhir Srivastava
Journal:  BMC Med Res Methodol       Date:  2002-02-28       Impact factor: 4.615

8.  Estimating the cumulative risk of false positive cancer screenings.

Authors:  Stuart G Baker; Diane Erwin; Barnett S Kramer
Journal:  BMC Med Res Methodol       Date:  2003-07-03       Impact factor: 4.615

9.  Statistical issues in randomized trials of cancer screening.

Authors:  Stuart G Baker; Barnett S Kramer; Philip C Prorok
Journal:  BMC Med Res Methodol       Date:  2002-09-19       Impact factor: 4.615

10.  The paired availability design for historical controls.

Authors:  S G Baker; K S Lindeman; B S Kramer
Journal:  BMC Med Res Methodol       Date:  2001-09-26       Impact factor: 4.615
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  24 in total

1.  RE: Leveraging Biospecimen Resources for Discovery or Validation of Markers for Early Cancer Detection.

Authors:  Stuart G Baker
Journal:  J Natl Cancer Inst       Date:  2015-09       Impact factor: 13.506

2.  Improving the quality of biomarker discovery research: the right samples and enough of them.

Authors:  Margaret S Pepe; Christopher I Li; Ziding Feng
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-04-02       Impact factor: 4.254

3.  Bias in estimating accuracy of a binary screening test with differential disease verification.

Authors:  Todd A Alonzo; John T Brinton; Brandy M Ringham; Deborah H Glueck
Journal:  Stat Med       Date:  2011-04-15       Impact factor: 2.373

4.  Additional thoughts on causal inference, probability theory, and graphical insights.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2013-11-10       Impact factor: 2.373

Review 5.  New paradigms in translational science research in cancer biomarkers.

Authors:  Paul D Wagner; Sudhir Srivastava
Journal:  Transl Res       Date:  2012-02-03       Impact factor: 7.012

6.  Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens.

Authors:  Hormuzd A Katki; Yan Li; David W Edelstein; Philip E Castle
Journal:  Stat Med       Date:  2011-12-04       Impact factor: 2.373

Review 7.  Better cancer biomarker discovery through better study design.

Authors:  Andrew Rundle; Habibul Ahsan; Paolo Vineis
Journal:  Eur J Clin Invest       Date:  2012-09-23       Impact factor: 4.686

8.  Nested case-control data analysis using weighted conditional logistic regression in The Environmental Determinants of Diabetes in the Young (TEDDY) study: A novel approach.

Authors:  Hye-Seung Lee; Kristian F Lynch; Jeffrey P Krischer
Journal:  Diabetes Metab Res Rev       Date:  2019-07-31       Impact factor: 4.876

Review 9.  The state of molecular biomarkers for the early detection of lung cancer.

Authors:  Mohamed Hassanein; J Clay Callison; Carol Callaway-Lane; Melinda C Aldrich; Eric L Grogan; Pierre P Massion
Journal:  Cancer Prev Res (Phila)       Date:  2012-06-11

10.  Cancer Screening Markers: A Simple Strategy to Substantially Reduce the Sample Size for Validation.

Authors:  Stuart G Baker
Journal:  Med Decis Making       Date:  2019-01-18       Impact factor: 2.583
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