Literature DB >> 22723719

Testing in a Prespecified Subgroup and the Intent-to-Treat Population.

Mark D Rothmann1, Jenny J Zhang, Laura Lu, Thomas R Fleming.   

Abstract

In many settings, testing has been proposed to assess the effect of an experimental regimen within a biomarker-positive subgroup where it is biologically plausible that benefit is stronger in such patients, and in the overall population that also includes biomarker-negative subjects less likely to benefit from that regimen. A statistically favorable result in the biomarker-positive subgroup would lead to a claim for that subgroup, whereas a statistically favorable result for the overall population would lead to a claim that includes both biomarker subgroups. The latter setting is problematic when biomarker-negative patients truly do not benefit from the experimental regimen. When it is prespecified that biomarker-negative patients should not be included in the primary analysis of treatment effect in biomarker-positive patients because of the likelihood that treatment effects would differ between the 2 subgroups, it is logically inconsistent to include biomarker-positive patients in the primary analysis of treatment effect in biomarker-negative patients.

Entities:  

Year:  2012        PMID: 22723719      PMCID: PMC3378054          DOI: 10.1177/0092861512436579

Source DB:  PubMed          Journal:  Drug Inf J        ISSN: 0092-8615


  5 in total

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Journal:  Clin Cancer Res       Date:  2005-11-01       Impact factor: 12.531

2.  New challenges for 21st century clinical trials.

Authors:  Richard Simon
Journal:  Clin Trials       Date:  2007       Impact factor: 2.486

3.  Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect.

Authors:  Wenyu Jiang; Boris Freidlin; Richard Simon
Journal:  J Natl Cancer Inst       Date:  2007-06-27       Impact factor: 13.506

4.  Use of genomic signatures in therapeutics development in oncology and other diseases.

Authors:  R Simon; S-J Wang
Journal:  Pharmacogenomics J       Date:  2006 May-Jun       Impact factor: 3.550

5.  Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset.

Authors:  Sue-Jane Wang; Robert T O'Neill; H M James Hung
Journal:  Pharm Stat       Date:  2007 Jul-Sep       Impact factor: 1.894

  5 in total
  7 in total

1.  Phase III clinical trials that integrate treatment and biomarker evaluation.

Authors:  Boris Freidlin; Zhuoxin Sun; Robert Gray; Edward L Korn
Journal:  J Clin Oncol       Date:  2013-04-08       Impact factor: 44.544

Review 2.  Phase III Precision Medicine Clinical Trial Designs That Integrate Treatment and Biomarker Evaluation.

Authors:  Mei-Yin C Polley; Edward L Korn; Boris Freidlin
Journal:  JCO Precis Oncol       Date:  2019-10-24

Review 3.  Clinical Benefit Scales and Trial Design: Some Statistical Issues.

Authors:  Edward L Korn; Carmen J Allegra; Boris Freidlin
Journal:  J Natl Cancer Inst       Date:  2022-09-09       Impact factor: 11.816

Review 4.  Biomarker enrichment strategies: matching trial design to biomarker credentials.

Authors:  Boris Freidlin; Edward L Korn
Journal:  Nat Rev Clin Oncol       Date:  2013-11-26       Impact factor: 66.675

5.  Auxiliary variable-enriched biomarker-stratified design.

Authors:  Ting Wang; Xiaofei Wang; Haibo Zhou; Jianwen Cai; Stephen L George
Journal:  Stat Med       Date:  2018-09-16       Impact factor: 2.373

6.  A Problematic Biomarker Trial Design.

Authors:  Boris Freidlin; Edward L Korn
Journal:  J Natl Cancer Inst       Date:  2022-02-07       Impact factor: 13.506

Review 7.  Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes.

Authors:  Julien Tanniou; Ingeborg van der Tweel; Steven Teerenstra; Kit C B Roes
Journal:  BMC Med Res Methodol       Date:  2016-02-18       Impact factor: 4.615

  7 in total

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