title: A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes.
abstract: BACKGROUND
  Response-adaptive randomisation designs have been proposed to improve the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosenberger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size.
  
  
  OBJECTIVE
  The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the estimated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by redesigning a clinical trial on multiple myeloma.
  
  
  METHODS
  To handle continuous monitoring of data, we propose a Bayesian response-adaptive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simulation study to assess and compare the performance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive - either frequentist or fully Bayesian - designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior distribution of the log hazard ratio were computed. The method is then illustrated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts.
  
  
  RESULTS
  As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mixture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules.
  
  
  CONCLUSIONS
  Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.
authors.forename: M
authors.initials: M
authors.lastname: Moatti
authors.affiliation: Sylvie Chevret, Biostatistics and Clinical Epidemiology (ECSTRA) Team, Paris Diderot University, Saint-Louis hospital, 1, avenue Claude Vellefaux, 75475 Paris Cedex 10, France, E-mail: sylvie.chevret@univ-paris-diderot.fr.
authors.forename: S
authors.initials: S
authors.lastname: Chevret
authors.forename: S
authors.initials: S
authors.lastname: Zohar
authors.forename: W F
authors.initials: WF
authors.lastname: Rosenberger
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D017326:Clinical Trials, Phase III as Topic; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004358:Drug Therapy / Q000379:methods; D006801:Humans; D009101:Multiple Myeloma / Q000401:mortality* / Q000628:therapy*; D016016:Proportional Hazards Models; D011897:Random Allocation; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents
keywords: Adaptive design; Bayesian; censored data; optimal allocation; response-adaptive randomisation