title: Phase II cancer clinical trials with heterogeneous patient populations.
abstract: The patient population for a Phase II trial often consists of multiple subgroups in terms of risk level. In this case, a popular design approach is to specify the response rate and the prevalence of each subgroup, to calculate the response rate of the whole population by the weighted average of the response rates across subgroups, and to choose a standard Phase II design such as Simon's optimal or minimax design to test the response rate for the whole population. In this case, although the prevalence of each subgroup is accurately specified, the observed prevalence among the accrued patients to the study may be quite different from the expected one because of the small sample size, which is typical in most Phase II trials. The fixed rejection value for a chosen standard Phase II design may be either too conservative (i.e., increasing the false rejection probability of the experimental therapy) if the trial accrues more high-risk patients than expected, or too anti-conservative (i.e., increasing the false acceptance probability of the experimental therapy) if the trial accrues more low-risk patients than expected. We can avoid such problems by adjusting the rejection values, depending on the observed prevalence from the trial. In this paper, we investigate the performance of the flexible designs compared with the standard design with fixed rejection values under various settings.
authors.affiliation: Cancer and Leukemia Group B Statistical Center and Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA. sinho.jung@duke.edu
authors.forename: Sin-Ho
authors.initials: SH
authors.lastname: Jung
authors.forename: Myron N
authors.initials: MN
authors.lastname: Chang
authors.forename: Sun J
authors.initials: SJ
authors.lastname: Kang
mesh_terms: D000465:Algorithms; D017322:Clinical Trials, Phase II as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015507:Drugs, Investigational / Q000627:therapeutic use; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy*; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size; D035703:Therapies, Investigational
chemical_list: D015507:Drugs, Investigational