title: A Bayesian response-adaptive trial in tuberculosis: The endTB trial.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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: 1 Department of Biostatistics, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.|2 Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy.
authors: Matteo
authors: M
authors: Cellamare
authors: 1 Department of Biostatistics, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.|3 Department of Computer Science and Statistics, The University of Rhode Island, Kingston, RI, USA.
authors: Steffen
authors: S
authors: Ventz
authors: 4 Department of Clinical Research, Epicentre-MSF, Paris, France.
authors: Elisabeth
authors: E
authors: Baudin
authors: 5 Harvard Medical School, Department of Global Health and Social Medicine, Boston, MA, USA.|6 Partners In Health, Boston, MA, USA.
authors: Carole D
authors: CD
authors: Mitnick
authors: 1 Department of Biostatistics, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA.
authors: Lorenzo
authors: L
authors: Trippa
mesh_terms: D000075522:Adaptive Clinical Trials as Topic; D000465:Algorithms; D000995:Antitubercular Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D003198:Computer Simulation; D064687:Diarylquinolines / Q000627:therapeutic use; D006801:Humans; D009593:Nitroimidazoles / Q000627:therapeutic use; D010080:Oxazoles / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D016896:Treatment Outcome; D018088:Tuberculosis, Multidrug-Resistant / Q000188:drug therapy*
chemical_list: D000995:Antitubercular Agents; D064687:Diarylquinolines; D009593:Nitroimidazoles; C516022:OPC-67683; D010080:Oxazoles; C493870:bedaquiline
keywords: Bayesian analysis; Tuberculosis; adaptive designs; clinical trial; preliminary outcomes