Emily C Zabor1, Alexander M Kaizer2, Elizabeth Garrett-Mayer3, Brian P Hobbs4. 1. Department of Quantitative Health Sciences & Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH. 2. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Anschutz Medical Campus, Aurora, CO. 3. Division of Biostatistics and Research Data Governance, American Society of Clinical Oncology, Alexandria, VA. 4. Department of Population Health, University of Texas-Austin, Austin, TX.
Abstract
PURPOSE: The customary approach to early-phase clinical trial design, where the focus is on identification of the maximum tolerated dose, is not always suitable for noncytotoxic or other targeted therapies. Many trials have continued to follow the 3 + 3 dose-escalation design, but with the addition of phase I dose-expansion cohorts to further characterize safety and assess efficacy. Dose-expansion cohorts are not always planned in advance nor rigorously designed. We introduce an approach to the design of phase I expansion cohorts on the basis of sequential predictive probability monitoring. METHODS: Two optimization criteria are proposed that allow investigators to stop for futility to preserve limited resources while maintaining traditional control of type I and type II errors. We demonstrate the use of these designs through simulation, and we elucidate their implementation with a redesign of the phase I expansion cohort for atezolizumab in metastatic urothelial carcinoma. RESULTS: A sequential predictive probability design outperforms Simon's two-stage designs and posterior probability monitoring with respect to both proposed optimization criteria. The Bayesian sequential predictive probability design yields increased power while significantly reducing the average sample size under the null hypothesis in the context of the case study, whereas the original study design yields too low type I error and power. The optimal efficiency design tended to have more desirable properties, subject to constraints on type I error and power, compared with the optimal accuracy design. CONCLUSION: The optimal efficiency design allows investigators to preserve limited financial resources and to maintain ethical standards by halting potentially large dose-expansion cohorts early in the absence of promising efficacy results, while maintaining traditional control of type I and II error rates.
PURPOSE: The customary approach to early-phase clinical trial design, where the focus is on identification of the maximum tolerated dose, is not always suitable for noncytotoxic or other targeted therapies. Many trials have continued to follow the 3 + 3 dose-escalation design, but with the addition of phase I dose-expansion cohorts to further characterize safety and assess efficacy. Dose-expansion cohorts are not always planned in advance nor rigorously designed. We introduce an approach to the design of phase I expansion cohorts on the basis of sequential predictive probability monitoring. METHODS: Two optimization criteria are proposed that allow investigators to stop for futility to preserve limited resources while maintaining traditional control of type I and type II errors. We demonstrate the use of these designs through simulation, and we elucidate their implementation with a redesign of the phase I expansion cohort for atezolizumab in metastatic urothelial carcinoma. RESULTS: A sequential predictive probability design outperforms Simon's two-stage designs and posterior probability monitoring with respect to both proposed optimization criteria. The Bayesian sequential predictive probability design yields increased power while significantly reducing the average sample size under the null hypothesis in the context of the case study, whereas the original study design yields too low type I error and power. The optimal efficiency design tended to have more desirable properties, subject to constraints on type I error and power, compared with the optimal accuracy design. CONCLUSION: The optimal efficiency design allows investigators to preserve limited financial resources and to maintain ethical standards by halting potentially large dose-expansion cohorts early in the absence of promising efficacy results, while maintaining traditional control of type I and II error rates.
Authors: Thomas Powles; Joseph Paul Eder; Gregg D Fine; Fadi S Braiteh; Yohann Loriot; Cristina Cruz; Joaquim Bellmunt; Howard A Burris; Daniel P Petrylak; Siew-leng Teng; Xiaodong Shen; Zachary Boyd; Priti S Hegde; Daniel S Chen; Nicholas J Vogelzang Journal: Nature Date: 2014-11-27 Impact factor: 49.962
Authors: Thomas Powles; Michiel S van der Heijden; Daniel Castellano; Matthew D Galsky; Yohann Loriot; Daniel P Petrylak; Osamu Ogawa; Se Hoon Park; Jae-Lyun Lee; Ugo De Giorgi; Martin Bögemann; Aristotelis Bamias; Bernhard J Eigl; Howard Gurney; Som D Mukherjee; Yves Fradet; Iwona Skoneczna; Marinos Tsiatas; Andrey Novikov; Cristina Suárez; André P Fay; Ignacio Duran; Andrea Necchi; Sophie Wildsmith; Philip He; Natasha Angra; Ashok K Gupta; Wendy Levin; Joaquim Bellmunt Journal: Lancet Oncol Date: 2020-09-21 Impact factor: 41.316