A Statistical Framework for Decision Making in Confirmatory Multipopulation Tailoring Clinical Trials

This article focuses on statistical analysis of clinical trials pursuing tailored therapy objectives, wherein evaluation of treatment effect occurs in the overall population as well as in a predefined subpopulation(s). The design and analysis principles presented provide a framework for decision making based on these novel multipopulation tailoring trial designs, considering the particular case of confirmatory trials. These principles include traditional multiple testing considerations, as well as 2 new analysis principles.

[1]  Mohammad F Huque,et al.  A flexible strategy for testing subgroups and overall population , 2009, Statistics in medicine.

[2]  B. Wiens.  A fixed sequence Bonferroni procedure for testing multiple endpoints , 2003 .

[3]  R. Simon,et al.  Adaptive Signature Design: An Adaptive Clinical Trial Design for Generating and Prospectively Testing A Gene Expression Signature for Sensitive Patients , 2005, Clinical Cancer Research.

[4]  O. Guilbaud,et al.  A recycling framework for the construction of Bonferroni‐based multiple tests , 2009, Statistics in medicine.

[5]  M H Gail,et al.  A comparison of the power of two tests for qualitative interactions. , 1993, Statistics in medicine.

[6]  Sue-Jane Wang,et al.  Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset , 2007, Pharmaceutical statistics.

[7]  R. Simon,et al.  On the efficiency of targeted clinical trials , 2005, Statistics in medicine.

[8]  Peter H. Westfall,et al.  Multiple testing methodology , 2009 .

[9]  G. Giaccone,et al.  SATURN: A double-blind, randomized, phase III study of maintenance erlotinib versus placebo following nonprogression with first-line platinum-based chemotherapy in patients with advanced NSCLC. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  Alex Dmitrienko,et al.  Design and Analysis Considerations in Clinical Trials With a Sensitive Subpopulation , 2010 .

[11]  M. Gail,et al.  Testing for qualitative interactions between treatment effects and patient subsets. , 1985, Biometrics.

[12]  Alex Dmitrienko,et al.  Chain Procedures: A Class of Flexible Closed Testing Procedures With Clinical Trial Applications , 2011 .

[13]  Sue-Jane Wang,et al.  Adaptive patient enrichment designs in therapeutic trials , 2009, Biometrical journal. Biometrische Zeitschrift.

[14]  B. Wiens,et al.  The Fallback Procedure for Evaluating a Single Family of Hypotheses , 2005, Journal of biopharmaceutical statistics.

[15]  George Y H Chi,et al.  A method for testing a prespecified subgroup in clinical trials , 2007, Statistics in medicine.

[16]  M. Huque,et al.  A flexible fixed-sequence testing method for hierarchically ordered correlated multiple endpoints in clinical trials , 2008 .

[17]  W. Brannath,et al.  A graphical approach to sequentially rejective multiple test procedures , 2009, Statistics in medicine.

[18]  R. Simon,et al.  Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect. , 2007, Journal of the National Cancer Institute.