Literature DB >> 28530853

Designing Clinical Trials That Accept New Arms: An Example in Metastatic Breast Cancer.

Steffen Ventz1, Brian M Alexander1, Giovanni Parmigiani1, Richard D Gelber1, Lorenzo Trippa1.   

Abstract

Purpose The majority of randomized oncology trials are two-arm studies that test the efficacy of new therapies against a standard of care, thereby assigning a large proportion of patients to nonexperimental therapies. In contrast, multiarm studies efficiently share a common control arm while evaluating multiple experimental therapies. A major bottleneck for traditional multiarm trials is the requirement that all therapies-often drugs from different companies-have to be available at the same time when the trial starts. We evaluate the potential gains of a platform design-the rolling-arms design-that adds and removes arms on a rolling basis. Methods We define the rolling-arms design with the goal of minimizing the complexity of random assignment and data analyses of a platform trial. We then evaluate its potential advantages in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer. Multiple pharmaceutical companies currently test CDK4/6 inhibitors in combination with letrozole in independent two-arm trials. We conducted a simulation study to quantify the reduction in sample size, number of patients treated with the standard of care, and the average time to treatment discovery if these therapies had been tested in a rolling-arms trial. Results A rolling-arms platform design with two to five experimental treatments can reduce the overall sample size requirement by up to 30% compared with standard two-arm studies. It assigns up to 60% fewer patients to the control arm compared with five independent trials that test distinct treatments. Moreover, under realistic scenarios, effective experimental treatments are discovered up to 15 months earlier compared with separate two-arm trials. Conclusion The rolling-arms platform design is applicable to a broad variety of diseases, and under realistic scenarios, it is substantially more efficient than standard two-arm randomized trials.

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Year:  2017        PMID: 28530853     DOI: 10.1200/JCO.2016.70.1169

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  11 in total

1.  To randomize, or not to randomize, that is the question: using data from prior clinical trials to guide future designs.

Authors:  Alyssa M Vanderbeek; Steffen Ventz; Rifaquat Rahman; Geoffrey Fell; Timothy F Cloughesy; Patrick Y Wen; Lorenzo Trippa; Brian M Alexander
Journal:  Neuro Oncol       Date:  2019-10-09       Impact factor: 12.300

2.  To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework.

Authors:  Kim May Lee; James Wason; Nigel Stallard
Journal:  Stat Med       Date:  2019-05-21       Impact factor: 2.373

3.  The clinical trials landscape for glioblastoma: is it adequate to develop new treatments?

Authors:  Alyssa M Vanderbeek; Rifaquat Rahman; Geoffrey Fell; Steffen Ventz; Tianqi Chen; Robert Redd; Giovanni Parmigiani; Timothy F Cloughesy; Patrick Y Wen; Lorenzo Trippa; Brian M Alexander
Journal:  Neuro Oncol       Date:  2018-07-05       Impact factor: 12.300

4.  Intergroup LEAP trial (S1612): A randomized phase 2/3 platform trial to test novel therapeutics in medically less fit older adults with acute myeloid leukemia.

Authors:  Roland B Walter; Laura C Michaelis; Megan Othus; Geoffrey L Uy; Jerald P Radich; Richard F Little; Sandi Hita; Lalit Saini; James M Foran; Aaron T Gerds; Heidi D Klepin; Annette E Hay; Sarit Assouline; Jeffrey E Lancet; Stephen Couban; Mark R Litzow; Richard M Stone; Harry P Erba
Journal:  Am J Hematol       Date:  2017-12-04       Impact factor: 10.047

5.  Inference in response-adaptive clinical trials when the enrolled population varies over time.

Authors:  Massimiliano Russo; Steffen Ventz; Victoria Wang; Lorenzo Trippa
Journal:  Biometrics       Date:  2021-10-21       Impact factor: 1.701

6.  The effects of releasing early results from ongoing clinical trials.

Authors:  Steffen Ventz; Sergio Bacallado; Rifaquat Rahman; Sara Tolaney; Jonathan D Schoenfeld; Brian M Alexander; Lorenzo Trippa
Journal:  Nat Commun       Date:  2021-02-05       Impact factor: 14.919

7.  Combining factorial and multi-arm multi-stage platform designs to evaluate multiple interventions efficiently.

Authors:  Ian R White; Babak Choodari-Oskooei; Matthew R Sydes; Brennan C Kahan; Leanne McCabe; Anna Turkova; Hanif Esmail; Diana M Gibb; Deborah Ford
Journal:  Clin Trials       Date:  2022-05-17       Impact factor: 2.599

8.  Emulating Control Arms for Cancer Clinical Trials Using External Cohorts Created From Electronic Health Record-Derived Real-World Data.

Authors:  Katherine Tan; Jonathan Bryan; Brian Segal; Lawrence Bellomo; Nate Nussbaum; Melisa Tucker; Aracelis Z Torres; Carrie Bennette; William Capra; Melissa Curtis; Rebecca A Miksad
Journal:  Clin Pharmacol Ther       Date:  2021-07-31       Impact factor: 6.903

9.  Adding new experimental arms to randomised clinical trials: Impact on error rates.

Authors:  Babak Choodari-Oskooei; Daniel J Bratton; Melissa R Gannon; Angela M Meade; Matthew R Sydes; Mahesh Kb Parmar
Journal:  Clin Trials       Date:  2020-02-17       Impact factor: 2.486

10.  Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials.

Authors:  Elias Laurin Meyer; Peter Mesenbrink; Cornelia Dunger-Baldauf; Ekkehard Glimm; Yuhan Li; Franz König
Journal:  Pharm Stat       Date:  2022-01-31       Impact factor: 1.234

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