ROC Analysis for Phase II Group Sequential Basket Clinical Trial


  • Sirao Wang Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington DC 20057, USA
  • Ao Yuan NIH Clinical Center, Rehabilitation Medicine Department, Bethesda, MD 20892, USA
  • Larry Tang NIH Clinical Center, Rehabilitation Medicine Department, Bethesda, MD 20892, USA
  • Hong Bin Fang Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington DC 20057, USA
  • Ming T. Tan Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington DC 20057, USA
  • Leighton Chan NIH Clinical Center, Rehabilitation Medicine Department, Bethesda, MD 20892, USA



Basket trial, group sequential clinical trial, nonparametric ROC curve, parametric ROC curve, phase II clinical trial.


The basket trial is a recent development in the clinical trial practice. It conducts the test of the same treatment on several different related diseases in a single trial, and has the advantage of reduced cost and enhanced efficiency. A natural question is how to assess the performance of the group sequential basket trial against the classical group sequential trial? To our knowledge, a formal assessment hasn’t been seen in the literature, and is the goal of this study. Specifically, we use the receiver operating characteristic curve to assess the performance of the mentioned two trials. We considered two cases, parametric and nonparametric settings. The former is efficient when the parametric model is correctly specified, but can bemis-leading if the model is incorrect; the latter is less efficient but is robust in that it cannot be wrong no matter what the true data generating model is. Simulation studies are conducted to evaluate the experiments, and it suggests that the group sequential basket trial generally outperforms the group sequential trial in either the parametric and nonparametric cases, and that the nonparametric method gives more accurate evaluation than the parametric one for moderate to large sample sizes.


Willyard C. Basket studies?will hold intricate data for cancer drug approvals. Nature Medicine 2013; (6): 655. DOI:

Redig AJ, Janne PA. Basket trials and the evolution of clinical trial design in an era of genomic medicine. Journal of Clinical Oncology 2015; (20): 975-977. DOI:

Beckman RA, Antonijevic Z, Kalamegham R, Chen C. Design for a phase 3 basket trial in multiple tumor types based on a putative biomarker. Clinical Cancer Research 2015; In press.

Chen C, Li N, Yuan S, Antonijevic Z, Kalamegham R, Beckman RA. Statistical design and consideration of a phase 3 basket trial for simultaneous investigation of multiple tumor types in one study 2016. DOI:

Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clinical Pharmacology & Therapeutics 2009; 97-100. DOI:

Kopetz S. Right Drug for the Right Patient: Hurdles and the path forward in colorectal cancer. ASCO educational book 2013. DOI:

Meador CB, Micheel CM, Levy MA, Lovly CM, Horn L, Warner JL, et al.Beyond histology: translating tumor genotypes into clinically effective targeted therapies. Clinical Cancer Research 2014; 20: 2264-2275. DOI:

Lacombe D, Burocka S, Bogaertsa J, Schoeffskib P, Golfinopoulosa V, Stuppa R. The dream and reality of histology agnostic cancer clinical trials. Molecular Oncology 2014; 8: 1057-1063. DOI:

Sleijfer S, Bogaerts J, Siu LL. Designing transformative clinical trials in the cancer genome era. Journal of Clinical Oncology 2013; 31: 1834-1841. DOI:

Demetri G, Becker R, Woodcock J, Doroshow J, Nisen P, Sommer J. Alternative trial designs based on tumor genetics/pathway characteristics instead of histology. Issue Brief: Conference on Clinical Cancer Research; conference-clinical-cancer-research 2011.

Metz CE. Basic principles of ROC analysis. In Seminars in Nuclear Medicine 1978; (4): 283-298. WB Saunders. DOI:

Zhou XH, Obuchowski AA, McClish DK. Statistical Methods in Diagnostic Medicine. 2011; Vol. 712: Wiley. com. DOI:

Tang LL, Zhou XH. Semiparametric separation curve approach for comparing correlated ROC data from multiple markers, Journal of Computational and Graphical Statistics 2012; 662-676. DOI:

Tang LL, Yuan A, Collins J, Che X, Chan L. Unified least squares methods for the evaluation of diagnostic tests with the gold standard 2016. DOI:

Pocock SJ. Group sequential methods in the design and analysis of clinical trials.Biometrika 1977; 191-199. DOI:

O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics 1979; 549-556. DOI:

Berry DA. Interim analysis in clinical trials: Classical vs. Bayesian approaches. Statistics in Medicine 1985; 521-26. DOI:

Wang SK, Tsaitis AA. Approximately optimal one-parameter boundaries for group sequential trials. Biometrics 1987; 193-200. DOI:

Moss AJ, Hall WJ, Cannom DS, et al. Improved survival with an implanted defibrillator in patients with coronary disease at high risk for ventricular arrhythmia. The New England Journal of Medicine 1996; 1933-1940. DOI:

Bellissant E, Duhamel JF, Guillot M, et al. The triangular test to assess the efficacy of metoclopramide in gastroesophageal reflux. Clinical Pharmacology and Therapeutics 1997; 377-384. DOI:

Tan M, Xiong X, Kutner MH. Clinical trial designs based on sequential conditional probability ratio tests and reverse stochastic curtailing. Biometrics 1998; 682-695. DOI:

Jennison C, Turnbull BW. Group sequential methods with applications to clinical trials, CRC Press Inc. (Boca Raton, FL) 2000. DOI:

Hung J, Wang S-J, O’Neill R. Statistical considerations for testing multiple endpoints in group sequential or adaptive clinical trials. Journal of Biopharmaceutical Statistics 2007; 1201-1210. DOI:

Huang P, Tan MT. Multistage nonparametric global statistical test: a solution to the Behrens-Fisher problem for multidimensional mixed outcomes. Statistics and Its Interface 2016; 9(3): 343-354. DOI:

Qin J, Lawless JL. Empirical likelihood and general estimating equations. Annals of Statistics 1994; 22: 300-325. DOI:

Owen AB. Empirical likelihood confidence regions. Annals of Statistics 1990; 18: 90-120. DOI:

Yuan A, He W, Wang B, Qin G. U-statistic with side information. Journal of Multivariate Analysis 2012; 20-38. DOI:

Hyman DM,et al. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. New England Journal of Medicine 2015; 373: 726-736. DOI:

Dadu R, Shah K, Busaidy NL, Waguespack SG, Habra MA, Ying AK, Hu MI, Bassett R, Jimenez C, Sherman SI, Cabanillas ME. Efficacy and tolerability of vemurafenib in patients with BRAFV600E-positive papillary thyroid cancer: MD Anderson Cancer Center off label experience. The Journal of Clinical Endocrinology & Metabolism 2014; 100(1): E77-E81. DOI:




How to Cite

Wang, S., Yuan, A., Tang, L., Fang, H. B., Tan, M. T., & Chan, L. (2017). ROC Analysis for Phase II Group Sequential Basket Clinical Trial. International Journal of Statistics in Medical Research, 6(1), 22–33.



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