Adaptive randomization for multiarm comparative clinical trials based on joint efficacytoxicity outcomes

Biometrics

Volumn 65, Issue 3, 2009, Pages 876-884

  Ji, Yuan ^a   Bekele, B Nebiyou ^a  

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

Author keywords

Assessment time; Augmented likelihood; Follow up; Latent model; Posterior

Indexed keywords

BIOMETRICS; TOXICITY;

APPROXIMATE BAYESIAN; ASSESSMENT TIME; AUGMENTED LIKELIHOOD; CLINICAL TRIAL; FOLLOW UP; LATENT MODELS; LOW TOXICITY; POSTERIOR; RANDOMISATION; TIME TO EVENTS;

RANDOM PROCESSES;

BAYESIAN ANALYSIS; COMPARATIVE STUDY; MOLECULAR ANALYSIS; TOXICITY;

ARTICLE; BAYES THEOREM; CANCER MODEL; CHRONIC LYMPHATIC LEUKEMIA; CLINICAL TRIAL; DRUG CYTOTOXICITY; FOLLOW UP; LEUKEMIA REMISSION; MULTICENTER STUDY; OUTCOME ASSESSMENT; PROBABILITY; RANDOMIZATION;

BIOMETRY; CONTROLLED CLINICAL TRIALS AS TOPIC; DATA INTERPRETATION, STATISTICAL; ENDPOINT DETERMINATION; HUMANS; OUTCOME ASSESSMENT (HEALTH CARE); PROPORTIONAL HAZARDS MODELS; RESEARCH DESIGN; TREATMENT OUTCOME;

EID: 70349254459     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2008.01175.x     Document Type: Article

References (13)

1. Berry, D. and Eick, S. (1995). Adaptive assignment versus balanced randomization in clinical trials: A decision analysis. Statistics in Medicine 14, 231 246.
2. Block, H. and Basu, A. (1974). A continuous bivariate exponential extension. Journal of the American Statistical Association 69, 1031 1037.
3. Cheung, Y. and Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56, 1177 1182.
4. Cheung, Y. and Thall, P. (2002). Monitoring the rates of composite events with censored data in phase II clinical trials. Biometrics 58, 89 97.
5. Cheung, Y., Inoue, L., Wathen, K., and Thall, P. (2006). Continuous Bayesian adaptive randomization based on event times with covariates. Statistics in Medicine 25, 55 70.
6. Clayton, D. and Cuzick, J. (1985). Multivariate generalizations of the proportional hazards model (with discussion). Journal of the Royal Statistical Society, Series A 148, 82 117.
7. Joseph, L., Theresa, G., and Louis, C. (1995). Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard. American Journal of Epidemiology 141, 263 272.
8. Oakes, D. (1989). Bivariate survival models induced by frailties. Journal of the American Statistical Association 84, 487 493.
9. Stamey, J., Seaman, J., and Young, D. (2004). Bayesian sample-size determination for inference on two binomial populations with no gold standard classifier. Statistics in Medicine 24, 2963 2976.
10. Tanner, M. and Wong, W. (1987). The calculation of posterior distributions by data augmentation (with discussion). Journal of the American Statistical Association 82, 528 550.
11. Thall, P. and Wathen, K. (2005). Covariate-adjusted adaptive randomization in a sarcoma trial with multi-stage treatments. Statistics in Medicine 24, 1947 1964.
12. Thall, P., Inoue, L., and Martin, T. (2002). Adaptive decision making in a lymphocyte infusion trial. Biometrics 58, 560 568.
13. Zhang, L. and Rosenberger, W. (2007). Response-adaptive randomization for survival trials. Journal of the Royal Statistical Society, Series C (Applied Statistics) 53, 153 165.