Literature DB >> 22463794

Biomarkers, subgroup evaluation, and clinical trial design.

Stuart G Baker1, Barnett S Kramer, Daniel J Sargent, Marco Bonetti.   

Abstract

Advances in clinical and basic sciences are raising the potential to use genetic and clinical biomarkers to identify a subgroup of patients who would most likely benefit from treatment, and to evaluate the benefit of treatment in that subgroup. To make full use of this potential, special clinical trial designs and analyses are needed. For identifying and evaluating a subgroup based on a single continuous biomarker, the most informative approach is the biomarker-analysis design, which is a randomized trial whose analysis involves estimation of the treatment benefit within patient groups defined with respect to various cutpoints or intervals of the biomarker. For identifying and evaluating a subgroup considering a range of possible markers, the adaptive signature design is recommended. In the adaptive signature design, participants are randomly split into training and test samples, a rule for creating the subgroup is formulated in the training sample, and treatment benefit is estimated in the test sample. The adaptive signature design can be usefully extended via the sliding-window subgroup plot that was originally developed for the biomarker-analysis design.

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Year:  2012        PMID: 22463794

Source DB:  PubMed          Journal:  Discov Med        ISSN: 1539-6509            Impact factor:   2.970


  15 in total

1.  Evaluating Prognostic Markers Using Relative Utility Curves and Test Tradeoffs.

Authors:  Stuart G Baker; Barnett S Kramer
Journal:  J Clin Oncol       Date:  2015-06-29       Impact factor: 44.544

2.  Identifying optimal biomarker combinations for treatment selection via a robust kernel method.

Authors:  Ying Huang; Youyi Fong
Journal:  Biometrics       Date:  2014-08-14       Impact factor: 2.571

3.  Evaluating Markers for Guiding Treatment.

Authors:  Stuart G Baker; Marco Bonetti
Journal:  J Natl Cancer Inst       Date:  2016-05-18       Impact factor: 13.506

4.  Clozapine and Psychosocial Function in Schizophrenia: A Systematic Review and Meta-Analysis.

Authors:  Andrew T Olagunju; Scott R Clark; Bernhard T Baune
Journal:  CNS Drugs       Date:  2018-11       Impact factor: 5.749

5.  Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes.

Authors:  Stuart G Baker; Barnett S Kramer
Journal:  Clin Trials       Date:  2014-11-10       Impact factor: 2.486

Review 6.  Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review.

Authors:  Miranta Antoniou; Ruwanthi Kolamunnage-Dona; Andrea L Jorgensen
Journal:  J Pers Med       Date:  2017-01-25

7.  Evaluating marker-guided treatment selection strategies.

Authors:  Roland A Matsouaka; Junlong Li; Tianxi Cai
Journal:  Biometrics       Date:  2014-04-29       Impact factor: 2.571

8.  Adaptive randomized phase II design for biomarker threshold selection and independent evaluation.

Authors:  Lindsay A Renfro; Christina M Coughlin; Axel M Grothey; Daniel J Sargent
Journal:  Chin Clin Oncol       Date:  2014-03-01

Review 9.  Incorporation of prognostic and predictive factors into glioma clinical trials.

Authors:  Derek R Johnson; Evanthia Galanis
Journal:  Curr Oncol Rep       Date:  2013-02       Impact factor: 5.075

10.  Biomarker evaluation in randomized trials: addressing different research questions.

Authors:  Stuart G Baker
Journal:  Stat Med       Date:  2014-10-15       Impact factor: 2.373

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