A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial

Statistics in Medicine

Volumn 30, Issue 11, 2011, Pages 1218-1229

  Yuan, Ying ^a   Huang, Xuelin ^a   Liu, Suyu ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Adaptive randomization; Balance covariates; Prognostic factor; Response adaptive

Indexed keywords

ACUTE GRANULOCYTIC LEUKEMIA; ARTICLE; BAYESIAN LEARNING; BAYESIAN RESPONSE ADAPTIVE COVARIATE BALANCED RANDOMIZATION DESIGN; CYTOGENETICS; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MEDICAL LITERATURE; METHODOLOGY; PHASE 2 CLINICAL TRIAL (TOPIC); PROBABILITY; PROGNOSIS; RANDOMIZATION; RECEIVER OPERATING CHARACTERISTIC; SIMULATION; STUDY DESIGN;

ANTINEOPLASTIC AGENTS; BAYES THEOREM; CLINICAL TRIALS, PHASE II AS TOPIC; COMPUTER SIMULATION; DISEASE-FREE SURVIVAL; HUMANS; LEUKEMIA, MYELOID, ACUTE; PROGNOSIS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN; SAMPLE SIZE;

EID: 79955576108     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4218     Document Type: Article

References (22)

1. Thompson WR. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 1933; 25:275-294.
2. Efron B. Forcing a sequential experiment to be balanced. Biometrika 1971; 58:403-417.
3. Wei LJ, Durham S. The randomized pay-the-winner rule in medical trials. Journal of the American Statistical Association 1978; 73:840-843.
4. Eisele JR. The doubly adaptive biased coin design for sequential clinical trials. Journal of Statistical Planning and Inference 1994; 38:249-261.
5. Berry DA, Eick SG. Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Statistics in Medicine 1995; 14:231-224.
6. Rosenberger WF, Stallard N, Ivanova A, Harper CH, Ricks ML. Optimal adaptive designs for binary response trials. Biometrics 2001; 57:909-913.
7. Thall PF, Inoue LYT, Martin TG. Adaptive decision making in a lymphocyte infusion trial. Biometrics 2002; 58:560-568.
8. Hu F, Rosenberger WF. The Theory of Response-adaptive Randomization in Clinical Trials. Wiley: New York, 2006.
9. Rosenberger WF, Sverdlov O. Handling covariates in the design of clinical trials. Statistical Science 2008; 23:404-419.
10. Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 1975; 31:103-115.
11. Wei LJ. An application of an urn model to the design of sequential controlled clinical trials. Journal of the American Statistical Association 1978; 73:559-563.
12. Atkinson AC. Optimal biased coin designs for sequential clinical trials with prognostic factors. Biometrika 1982; 69:61-67.
13. Signorini DF, Leung O, Simes RJ, Beller E, Gebski VJ. Dynamic balanced randomization for clinical trials. Statistics in Medicine 1993; 12:2343-2350.
14. Heritier S, Gebski V, Pillai A. Dynamic balancing randomization in controlled clinical trials. Statistics in Medicine 2005; 24:3729-3741.
15. Scott NW, McPherson GC, Ramsay CR, Campbell MK. The method of minimization for allocation to clinical trials: a review. Controlled Clinical Trials 2002; 23:662-674.
16. McEntegart D. The pursuit of balance using stratified and dynamic randomization techniques: an overview. Drug Information Journal 2003; 37:293-308.
17. Atkinson AC, Biswas A. Adaptive biased-coin designs for skewing the allocation proportion in clinical trials with normal response. Statistics in Medicine 2005; 24:2477-2492.
18. Ning J, Huang X. Response-adaptive randomization for clinical trials with adjustment for covariate imbalance. Statistics in Medicine 2010; 29:1761-1768.
19. Jennison C, Turnbull B. Group Sequential Methods with Applications to Clinical Trials. Chapman & Hall/CRC: London, 2000.
20. Karrison T, Huo D, Chappell R. Group sequential, response-adaptive designs for randomized clinical trials. Controlled Clinical Trials 2003; 24:506-522.
21. R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2010.
22. Coad DS. A comparative study of some data-dependent allocation rules for Bernoulli data. The Journal of Statistical Computation and Simulation 1992; 40:219-231.