title: Evaluation of a multi-arm multi-stage Bayesian design for phase II drug selection trials - an example in hemato-oncology.
abstract: Multi-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure.
  
  
  
  We redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds.
  
  
  
  Our findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3).
  
  
  
  Adaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates.
  
  
  
  ClinicalTrials.gov Identifier: NCT01342692 .
authors.affiliation: Biostatistics and Clinical Epidemiology team (ECSTRA), of the Center of Research on Epidemiology and Biostatistics Sorbonne Paris Cité (CRESS; INSERM UMR 1153), Paris Diderot University, SBIM- Hôpital Saint Louis; 1, av Claude Vellefaux 75010, Paris, France.|École Normale Supérieure de Lyon, 46 Allée d'Italie, 69007, Lyon, France.
authors.forename: Louis
authors.initials: L
authors.lastname: Jacob
authors.affiliation: École Nationale de la statistique et de l'analyse de l'information, Rue Blaise Pascal, Rennes, France.
authors.forename: Maria
authors.initials: M
authors.lastname: Uvarova
authors.affiliation: École Nationale de la statistique et de l'analyse de l'information, Rue Blaise Pascal, Rennes, France.
authors.forename: Sandrine
authors.initials: S
authors.lastname: Boulet
authors.affiliation: École Nationale de la statistique et de l'analyse de l'information, Rue Blaise Pascal, Rennes, France.
authors.forename: Inva
authors.initials: I
authors.lastname: Begaj
authors.affiliation: Biostatistics and Clinical Epidemiology team (ECSTRA), of the Center of Research on Epidemiology and Biostatistics Sorbonne Paris Cité (CRESS; INSERM UMR 1153), Paris Diderot University, SBIM- Hôpital Saint Louis; 1, av Claude Vellefaux 75010, Paris, France. sylvie.chevret@paris7.jussieu.fr.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chevret
mesh_terms: D000328:Adult; D001374:Azacitidine / Q000627:therapeutic use*; D001499:Bayes Theorem; D004359:Drug Therapy, Combination; D004791:Enzyme Inhibitors / Q000627:therapeutic use; D006801:Humans; D009190:Myelodysplastic Syndromes / Q000188:drug therapy*; D012107:Research Design; D018401:Sample Size; D014635:Valproic Acid / Q000627:therapeutic use; D055815:Young Adult
chemical_list: D004791:Enzyme Inhibitors; D014635:Valproic Acid; D001374:Azacitidine
keywords: Adaptive design; Bayesian; Drop/select drug; MAMS