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Abstract
Background Two- or three-stage designs are commonly used in phase II cancer clinical trials. These designs possess good frequentist properties and allow early termination of the trial when the interim data indicate that the experimental regimen is inefficacious. The rigid study design, however, can be difficult to follow exactly because the response has to be evaluated at prespecified fixed number of patients.
Purpose Our goal is to develop an efficient and flexible design that possesses desirable statistical properties.
Methods A flexible design based on Bayesian predictive probability and the minimax criterion is constructed. A three-dimensional search algorithm is implemented to determine the design parameters.
Results The new design controls type I and type II error rates, and allows continuous monitoring of the trial outcome. Consequently, under the null hypothesis when the experimental treatment is not efficacious, the design is more efficient in stopping the trial earlier, which results in a smaller expected sample size. Exact computation and simulation studies demonstrate that the predictive probability design possesses good operating characteristics.
Limitations The predictive probability design is more computationally intensive than two- or three-stage designs. Similar to all designs with early stopping due to futility, the resulting estimate of treatment efficacy may be biased.
Conclusions The predictive probability design is efficient and remains robust in controlling type I and type II error rates when the trial conduct deviates from the original design. It is more adaptable than traditional multi-stage designs in evaluating the study outcome, hence, it is easier to implement. S-PLUS/R programs are provided to assist the study design. Clinical Trials 2008; 5: 93—106. http://ctj.sagepub.com
|
Green S. , Benedetti J. , Crowley J. Clinical trials in oncology, 2nd edn. Boca, Chapman & Hall/ CRC , Raton, FL, 2002. Google Scholar | Crossref | |
|
Palesch YY , Tilley BC , Sackett DL , Johnson KC , Woolson R. Applying a phase II futility study design to therapeutic stroke trials . Stroke 2005; 36: 2410-14. Google Scholar | Crossref | Medline | ISI | |
|
Thall PF , Simon RM Recent developments in the design of phase II clinical trials. Cancer Treat Res 1995; 75: 49-71. Google Scholar | Crossref | Medline | |
|
Kramar A. , Potvin D. , Hill C. Multistage designs for phase II clinical trials: statistical issues in cancer research. Br J Cancer, 1996; 74: 1317-20. Google Scholar | Crossref | Medline | ISI | |
|
Mariani L. , Marubini E. Design and analysis of phase II cancer trials: a review of statistical methods and guidelines for medical researchers. Int Stat Rev 1996; 64(1): 61-88. Google Scholar | Crossref | ISI | |
|
Lee JJ , Feng L. Randomized phase II designs in cancer clinical trials: current status and future directions. J Clin Oncol 2005; 23: 4450-57. Google Scholar | Crossref | Medline | ISI | |
|
Gehan EA The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. J Chronic Dis 1961; 13: 346-53. Google Scholar | Crossref | Medline | |
|
Simon R. Optimal two-stage designs for phase II clinical trials. Cont Clin Trials 1989; 10: 1-10. Google Scholar | Crossref | Medline | |
|
Fleming TR One-sample multiple testing procedure for phase II clinical trials . Biometrics 1982; 38: 143-51. Google Scholar | Crossref | Medline | ISI | |
|
Lee YJ Phase II trials in cancer: present status and analysis methods. Drugs Exp Clin Res 1986; 12: 57-71. Google Scholar | Medline | |
|
Ensign LG , Gehan EA , Kamen DS , Thall, PF An optimal three-stage design for phase II clinical trials. Stat Med 1994; 13: 1727-36. Google Scholar | Crossref | Medline | ISI | |
|
Chen TT Optimal three-stage designs for phase II cancer clinical trials. Stat Med 1997; 16: 2701-11. Google Scholar | Crossref | Medline | ISI | |
|
Jung SH , Carey M. , Kim KM Graphical search for two-stage designs for phase II clinical trials . Cont Clin Trials 2001; 22: 367-72. Google Scholar | Crossref | Medline | |
|
Jung SH , Lee T. , Kim KM , George SL Admissible two-stage designs for phase II cancer clinical trials. Stat Med 2004; 23: 561-9. Google Scholar | Crossref | Medline | ISI | |
|
Green SJ , Dahlberg S. Planned versus attained design in phase II clinical trials. Stat Med 1992; 11: 853-62. Google Scholar | Crossref | Medline | ISI | |
|
Herndon II , JE. A design alternative for two-stage, phase II, multicenter cancer clinical trials. Cont Clin Trials 1998; 19: 440-50. Google Scholar | Crossref | Medline | |
|
Chen TT , Ng TH Optimal flexible designs in phase II clinical trials. Stat Med 1998; 17: 2301-12. Google Scholar | Crossref | Medline | ISI | |
|
Korn EL , Simon R. Data monitoring committees and problems of lower-than-expected accrual or events rates. Cont Clin Trials 1996; 17: 526-35. Google Scholar | Crossref | Medline | |
|
Berry DA Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin Trials 2005; 2: 295-300; discussion 301-4, 364-78. Google Scholar | SAGE Journals | ISI | |
|
Berry DA Clinical trials: is the Bayesian approach ready for prime time? Yes! Stroke 2005; 36: 1621-22. Google Scholar | Crossref | Medline | ISI | |
|
Sylvester RJ A Bayesian approach to the design of phase II clinical trials. Biometrics 1998; 44: 823-36. Google Scholar | Crossref | ISI | |
|
Thall PF , Simon R. A Bayesian approach to establishing sample size and monitoring criteria for phase II clinical trials. Cont Clin Trials 1994; 15: 463-481. Google Scholar | Crossref | Medline | |
|
Thall PF , Simon R. Practical Bayesian guidelines for phase IIB clinical trials. Biometrics 1994; 50: 337-49. Google Scholar | Crossref | Medline | ISI | |
|
Heitjan DF Bayesian interim analysis of phase II cancer clinical trials. Stat Med 1997; 16: 1791-802. Google Scholar | Crossref | Medline | ISI | |
|
Tan SB , Machin D. Bayesian two-stage designs for phase II clinical trials. Stat Med 2002; 21: 1991-2012. Google Scholar | Crossref | Medline | ISI | |
|
Mayo MS , Gajewski BJ Bayesian sample size calculations in phase II clinical trials using informative conjugate priors. Cont Clin Trials 2004; 25: 157-67. Google Scholar | Crossref | Medline | |
|
Gajewski BJ , Mayo MS Bayesian sample size calculations in phase II clinical trials using a mixture of informative priors. Stat Med 2006; 25: 2554-66. Google Scholar | Crossref | Medline | ISI | |
|
Spiegelhalter DJ , Abrams KR , Myles JP Bayesian Approaches to Clinical Trials and Health-Care Evaluation , John Wiley & Sons, Chichester , West Sussex, 2004. Google Scholar | |
|
Choi SC , Smith PJ , Becker DP Early decision in clinical trials when the treatment differences are small. Experience of a controlled trial in head trauma. Cont Clin Trials 1985; 6: 280-88. Google Scholar | Crossref | Medline | |
|
Choi SC , Pepple PA Monitoring clinical trials based on predictive probability of significance . Biometrics 1989; 45: 317-23. Google Scholar | Crossref | Medline | ISI | |
|
Spiegelhalter DJ , Freedman LS , Blackburn PR Monitoring clinical trials: conditional or predictive power? Cont Clin Trials 1986; 7: 8-17. Google Scholar | Crossref | Medline | |
|
Spiegelhalter DJ , Freedman LS A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion. Stat Med 1986; 5: 1-13. Google Scholar | Crossref | Medline | ISI | |
|
Grieve AP Predictive probability in clinical trials. Biometrics 1991; 47: 323-30. Google Scholar | Crossref | Medline | ISI | |
|
Johns D. , Andersen JS Use of predictive probabilities in phase II and phase III clinical trials . J Biopharm Stat 1999; 9: 67-79. Google Scholar | Crossref | Medline | |
|
Gould AL Timing of futility analyses for proof of concept trials. Stat Med 2005; 24: 1815-35. Google Scholar | Crossref | Medline | ISI | |
|
Berry DA A guide to drug discovery: Bayesian clinical trials. Nature Reviews Drug Discovery 2006; 5: 27-36. Google Scholar | Crossref | Medline | ISI | |
|
Herson J. Predictive probability early termination plans for phase II clinical trials . Biometrics 1979; 35: 775-83. Google Scholar | Crossref | Medline | ISI | |
|
Schultz JR , Nichol FR , Elfring GL , Weed SD Multiple-stage procedures for drug screening. Biometrics 1973; 29: 293-300. Google Scholar | Crossref | Medline | ISI | |
|
Lee JJ , Tu ZN A versatile one-dimensional distribution plot: the BLiP plot. The American Statistician 1997; 51: 353-58. Google Scholar | ISI | |
|
Spiegelhalter DJ , Freedman LS , Parmar MKB. Bayesian approaches to randomized trials . Journal of the Royal Statistical Society Series A 1994; 157: 357-416. Google Scholar | Crossref | ISI | |
|
Grossman J. , Parmar MKB , Spiegelhalter DJ , Freedman LS A unified method for monitoring and analysing controlled trials. Statistics in Medicine 1994; 13: 1815-26. Google Scholar | Crossref | Medline | ISI | |
|
Spiegelhalter DJ , Myles JP , Jones DR , Abrams KR Bayesian methods in health technology assessment: A review. Health Technology Assessment 2000 ; 4: 1-121. Google Scholar | Medline | |
|
Stallard N. , Whitehead J. , Cleall S. Decision-making in a phase II clinical trial: a new approach combining Bayesian and frequentist concepts. Pharmaceutical Statistics 2005; 4: 119-28. Google Scholar | Crossref | ISI | |
|
Wang YG , Leung DHY , Li M. , Tan SB Bayesian designs with frequentist and Bayesian error rate considerations . Statistical Methods in Medical Research 2005; 14: 445-56. Google Scholar | SAGE Journals | ISI |
