Picking the winners in a sea of plenty. 2002

Howard I Scher, and Glenn Heller
Genitourinary Oncology Service, Division of Solid Tumor Oncology, Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA.

OBJECTIVE Selecting an experimental arm for a Phase III trial is based on the results of Phase II investigations. Historical results show that this paradigm leads to the failure of many experimental therapies in the Phase III setting. This is the result of failures in the Phase II design that include differences in the patient populations and basing sample size determinations on levels of benefit derived from surrogate end points that do not accurately reflect the end point of interest in the Phase III study. An additional factor is how to ensure that the experimental therapy chosen was the best available at the time. METHODS We consider castrate metastatic prostate cancer, for which multiple regimens appear to have similar activity at this time. To assess superiority, we use a randomized Phase II/III design developed by Schaid et al. (D. J. Schaid et al., Biometrika, 77: 507-513, 1990) that allows multiple treatments to be tested at the same time and bases the determination to proceed from the Phase II study on the same clinical end point evaluated in the same population as the Phase III trial. A concurrent control group is also treated. RESULTS We demonstrate the integrative Phase II/III clinical trial design to evaluate two, three, or four experimental treatments with a survival-based end point in the same patient population. It includes a concurrent control in both the Phase II and Phase III portions of the study. The sample sizes in the Phase II component of the trial are comparable with those found in conventional single-arm Phase II trials. CONCLUSIONS The proposed design is valuable in situations where multiple regimens are available that appear worthy of evaluation in the Phase III setting, and where there is no adequate short-term surrogate end point for survival. The design is also useful in the evaluation of cytostatic agents where traditional response parameters may not identify potentially active drugs or, as is the case in advanced prostate cancer, in the evaluation of therapies that have a direct effect on prostate-specific antigen with an uncertain effect on survival.

UI MeSH Term Description Entries
D008297 Male Males
D011471 Prostatic Neoplasms Tumors or cancer of the PROSTATE. Cancer of Prostate,Prostate Cancer,Cancer of the Prostate,Neoplasms, Prostate,Neoplasms, Prostatic,Prostate Neoplasms,Prostatic Cancer,Cancer, Prostate,Cancer, Prostatic,Cancers, Prostate,Cancers, Prostatic,Neoplasm, Prostate,Neoplasm, Prostatic,Prostate Cancers,Prostate Neoplasm,Prostatic Cancers,Prostatic Neoplasm
D012107 Research Design A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly. Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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