BACKGROUND: Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. Targeted therapies, however, have created major challenges in the design, conduct, and analysis of clinical trials. In traditional clinical trials, treatment effects for various biomarkers are typically evaluated in an exploratory fashion and only limited information about the predictive values of biomarkers obtained. PURPOSE: New study designs are required, which effectively evaluate both the diagnostic and the therapeutic implication of biomarkers. METHODS: The Bayesian approach provides a useful framework for optimizing the clinical trial design by directly integrating information about biomarkers and clinical outcomes as they become available. We propose a Bayesian covariate-adjusted response-adaptive randomization design, which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach. RESULTS: A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. The simulation studies show that the proposed design efficiently identifies patients who benefit most from a targeted therapy and that there are substantial savings in the sample size requirements when compared to alternative designs. LIMITATIONS: The design does not control for the type I error in the traditional sense and a positive result should be confirmed by conducting an independent phase III study focusing on the selected biomarker profile groups. CONCLUSIONS: We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.
BACKGROUND: Targeted therapies are becoming increasingly important for the treatment of various diseases. Biomarkers are a critical component of a targeted therapy as they can be used to identify patients who are more likely to benefit from a treatment. Targeted therapies, however, have created major challenges in the design, conduct, and analysis of clinical trials. In traditional clinical trials, treatment effects for various biomarkers are typically evaluated in an exploratory fashion and only limited information about the predictive values of biomarkers obtained. PURPOSE: New study designs are required, which effectively evaluate both the diagnostic and the therapeutic implication of biomarkers. METHODS: The Bayesian approach provides a useful framework for optimizing the clinical trial design by directly integrating information about biomarkers and clinical outcomes as they become available. We propose a Bayesian covariate-adjusted response-adaptive randomization design, which utilizes individual biomarker profiles and patient's clinical outcomes as they become available during the course of the trial, to assign the most efficacious treatment to individual patients. Predictive biomarker subgroups are determined adaptively using a partial least squares regression approach. RESULTS: A series of simulation studies were conducted to examine the operating characteristics of the proposed study design. The simulation studies show that the proposed design efficiently identifies patients who benefit most from a targeted therapy and that there are substantial savings in the sample size requirements when compared to alternative designs. LIMITATIONS: The design does not control for the type I error in the traditional sense and a positive result should be confirmed by conducting an independent phase III study focusing on the selected biomarker profile groups. CONCLUSIONS: We conclude that the proposed design may serve a useful role in the early efficacy phase of targeted therapy development.
Authors: Margaret A Shipp; Ken N Ross; Pablo Tamayo; Andrew P Weng; Jeffery L Kutok; Ricardo C T Aguiar; Michelle Gaasenbeek; Michael Angelo; Michael Reich; Geraldine S Pinkus; Tane S Ray; Margaret A Koval; Kim W Last; Andrew Norton; T Andrew Lister; Jill Mesirov; Donna S Neuberg; Eric S Lander; Jon C Aster; Todd R Golub Journal: Nat Med Date: 2002-01 Impact factor: 53.440
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: BMJ Date: 2020-06-17
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
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: Trials Date: 2020-06-17 Impact factor: 2.279