Literature DB >> 21516572

Population enrichment designs: case study of a large multinational trial.

Cyrus R Mehta1, Ping Gao.   

Abstract

A method is proposed for modifying a group-sequential clinical trial by restricting future enrollment to a subgroup and possibly altering the sample size of the subgroup, based on an interim analysis of the data already obtained. The method provides strong control of type 1 error without requiring prespecification of the list of possible subgroups or of the decision rule for selecting among them. Nevertheless, for regulatory submissions it is recommended that the subgroups and decision rule be prespecified. The method is applied to a large cardiology trial in which the subgroups are prespecified and the decision rules for subgroup selection and sample size alteration are based on conditional power. It is shown by simulation that substantial gains in power can be attained if there is a subgroup by treatment interaction.

Mesh:

Year:  2011        PMID: 21516572     DOI: 10.1080/10543406.2011.554129

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  10 in total

1.  Using Bayesian modeling in frequentist adaptive enrichment designs.

Authors:  Noah Simon; Richard Simon
Journal:  Biostatistics       Date:  2018-01-01       Impact factor: 5.899

Review 2.  Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs.

Authors:  Ciara Nugent; Wentian Guo; Peter Müller; Yuan Ji
Journal:  JCO Precis Oncol       Date:  2019-10-24

3.  Stratification, Hypothesis Testing, and Clinical Trial Simulation in Pediatric Drug Development.

Authors:  Ann W McMahon; Kevin Watt; Jian Wang; Dionna Green; Ram Tiwari; Gilbert J Burckart
Journal:  Ther Innov Regul Sci       Date:  2016-06-02       Impact factor: 1.778

4.  Optimal decision rules for biomarker-based subgroup selection for a targeted therapy in oncology.

Authors:  Johannes Krisam; Meinhard Kieser
Journal:  Int J Mol Sci       Date:  2015-05-07       Impact factor: 5.923

5.  Inference for multimarker adaptive enrichment trials.

Authors:  Richard Simon; Noah Simon
Journal:  Stat Med       Date:  2017-08-10       Impact factor: 2.373

6.  Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

Authors:  Peter Bauer; Frank Bretz; Vladimir Dragalin; Franz König; Gernot Wassmer
Journal:  Stat Med       Date:  2015-03-16       Impact factor: 2.373

Review 7.  Clinical trial designs incorporating predictive biomarkers.

Authors:  Lindsay A Renfro; Himel Mallick; Ming-Wen An; Daniel J Sargent; Sumithra J Mandrekar
Journal:  Cancer Treat Rev       Date:  2016-01-05       Impact factor: 12.111

8.  Designing and analyzing clinical trials for personalized medicine via Bayesian models.

Authors:  Chuanwu Zhang; Matthew S Mayo; Jo A Wick; Byron J Gajewski
Journal:  Pharm Stat       Date:  2021-01-19       Impact factor: 1.894

Review 9.  Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review.

Authors:  Thomas Ondra; Alex Dmitrienko; Tim Friede; Alexandra Graf; Frank Miller; Nigel Stallard; Martin Posch
Journal:  J Biopharm Stat       Date:  2016       Impact factor: 1.051

10.  Enrichment Bayesian design for randomized clinical trials using categorical biomarkers and a binary outcome.

Authors:  Valentin Vinnat; Sylvie Chevret
Journal:  BMC Med Res Methodol       Date:  2022-02-27       Impact factor: 4.615

  10 in total

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