Matteo Cellamare1,2, Steffen Ventz1,3, Elisabeth Baudin4, Carole D Mitnick5,6, Lorenzo Trippa1. 1. 1 Department of Biostatistics, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA. 2. 2 Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy. 3. 3 Department of Computer Science and Statistics, The University of Rhode Island, Kingston, RI, USA. 4. 4 Department of Clinical Research, Epicentre-MSF, Paris, France. 5. 5 Harvard Medical School, Department of Global Health and Social Medicine, Boston, MA, USA. 6. 6 Partners In Health, Boston, MA, USA.
Abstract
PURPOSE: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. METHODS: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios. RESULTS: When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm. CONCLUSION: Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.
RCT Entities:
PURPOSE: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. METHODS: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosispatients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios. RESULTS: When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm. CONCLUSION: Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.
Authors: Patrick P J Phillips; Carole D Mitnick; James D Neaton; Payam Nahid; Christian Lienhardt; Andrew J Nunn Journal: PLoS Med Date: 2019-03-22 Impact factor: 11.069
Authors: Koen B Pouwels; Mo Yin; Christopher C Butler; Ben S Cooper; Sarah Wordsworth; A Sarah Walker; Julie V Robotham Journal: BMC Med Date: 2019-06-21 Impact factor: 8.775
Authors: Leanne McCabe; Ian R White; Nguyen Van Vinh Chau; Eleanor Barnes; Sarah L Pett; Graham S Cooke; A Sarah Walker Journal: Trials Date: 2020-05-18 Impact factor: 2.279
Authors: Christian Lienhardt; Andrew Nunn; Richard Chaisson; Andrew A Vernon; Matteo Zignol; Payam Nahid; Eric Delaporte; Tereza Kasaeva Journal: PLoS Med Date: 2020-02-27 Impact factor: 11.069
Authors: S E McAnaw; A C Hesseling; J A Seddon; K E Dooley; A J Garcia-Prats; S Kim; H E Jenkins; H S Schaaf; T R Sterling; C R Horsburgh Journal: Int J Infect Dis Date: 2016-12-09 Impact factor: 3.623