Tests and classification methods in adaptive designs with applications

References

• T. Barrett, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C.L. Robertson, N. Serova, S. Davis, and A. Soboleva, NCBI GEO: Archive for functional genomics data sets update, Nucl. Acids Res. 41 (2013), pp. 991–995.
   PubMed Web of Science ®Google Scholar
• P.L. Bartlett, The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network, IEEE Trans. Inf. Theory 44 (1998), pp. 525–536.
   Web of Science ®Google Scholar
• L. Breiman, Pasting bites together for prediction in large data sets and on-line, Tech. Rep., Dept. Statistics, Univ. California, Berkeley, 1997.
   Google Scholar
• L. Breiman, Random forests, Mach. Learn. 45 (2001), pp. 5–32.
   Web of Science ®Google Scholar
• C. Chen, N. Li, and Y. Shentua, Adaptive informational design of confirmatory phase III trials with an uncertain biomarker effect to improve the probability of success, Stat. Biopharm. Res. 8 (2016), pp. 237–247.
   Web of Science ®Google Scholar
• T. Chen and C. Guestrin, Xgboost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, 2016, pp. 785–794.
   Google Scholar
• S.C. Chow and M. Chang, Adaptive design methods in clinical trials: A review. Orphanet J. Rare Dis. 3 (2008), p. 11.
   PubMed Web of Science ®Google Scholar
• S.C. Chow, M. Chang, and A. Pong, Statistical consideration of adaptive methods in clinical development, J. Biopharm. Stat. 15 (2005), pp. 575–591.
   PubMed Web of Science ®Google Scholar
• C. Cortes and V. Vapnik, Support vector networks, Mach. Learn. 20 (1992), pp. 273–297.
   Google Scholar
• A. Dhammika and C. Javier, Mining data to find subsets of high activity, J. Stat. Plan. Inference 122 (2004), pp. 23–41.
   Web of Science ®Google Scholar
• B. Efron, Forcing a sequential experiment to be balanced, Biometrika 58 (1971), pp. 403–417.
   Web of Science ®Google Scholar
• I. Ezkurdia, D. Juan, J.M. Rodriguez, A. Frankish, M. Diekhans, J. Harrow, J. Vazquez, A. Valencia, and M.L. Tress, Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes, Hum. Mol. Genet. 23 (2014), pp. 5866–5878.
   PubMed Web of Science ®Google Scholar
• J.C. Foster, J.M.G. Taylor, and S.J. Ruberg, Subgroup identification from randomized clinical trial data, Stat. Med. 30 (2011), pp. 2867–880.
   PubMed Web of Science ®Google Scholar
• B. Freidlin and R. Simon, Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients, J. Am. Stat. Assoc. 11 (2005), pp. 7872–7878.
   Google Scholar
• Y. Freund and R.E. Schapire, A decision-theoretic generalization of online learning and an application to boosting, J. Comput. Syst. Sci. 55 (1997), pp. 119–139.
   Web of Science ®Google Scholar
• J.H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Stat. 29 (2001), pp. 1189–1232.
   Web of Science ®Google Scholar
• J.H. Friedman, Stochastic gradient boosting, Comput. Stat. Data Anal. 38 (2002), pp. 367–378.
   Web of Science ®Google Scholar
• J.H. Friedman, T. Hastie, and R. Tibshirani, Additive logistic regression: A statistical view of boosting, Ann. Stat. 28 (2000), pp. 337–374.
   Web of Science ®Google Scholar
• K.K. Gordon Lan and D.L. DeMets, Group sequential procedures: Calendar versus information time, Stat. Med. 8 (1978), pp. 1191–1198.
   Google Scholar
• R.A. Irizarry, B. Hobbs, and F. Collin, Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics 4 (2003), pp. 249–264.
   PubMed Web of Science ®Google Scholar
• C. Jennison and B.W. Turnbull, Group Sequential Methods with Applications to Clinical Trials, Chapman and Hall/CRC, New York, NY, 1999.
   Google Scholar
• J.W. Lee, J.B. Lee, M. Park, and S.H. Song, An extensive comparison of recent classification tools applied to microarray data, Comput. Stat. Data Anal. 48 (2005), pp. 869–885.
   Web of Science ®Google Scholar
• A. Liaw and M. Wiener, Classification and regression by randomForest, R News 2 (2002), pp. 18–22.
   Google Scholar
• I. Lipkovich, A. Dmitrienko, and J. Denne, Subgroup identification based on differential effect search-a recursive partitioning method for establishing response to treatment in patient subpopulations, Stat. Med. 30 (2011), pp. 2601–2621.
   PubMed Web of Science ®Google Scholar
• Q. Liu, M. Proschan, and G.W. Pledger, A unified theory of two-stage adaptive designs, J. Am. Stat. Assoc. 97 (1999), pp. 1034–1041.
   Google Scholar
• M. Posch and P. Bauer, Adaptive two-stage designs and the conditional error function, Biom. J. 41 (1999), pp. 689–696.
   Web of Science ®Google Scholar
• B. Rosner, On the detection of many outliers, Technometrics 17 (1975), pp. 221–227.
   Web of Science ®Google Scholar
• J. Shawe-Taylor, P.L. Bartlett, R.C. Williamson, and M. Anthony, Structural risk minimization over data-dependent hierarchies, IEEE Trans. Inf. Theory 44 (1998), pp. 1926–1940.
   Web of Science ®Google Scholar
• J. Shawe-Taylor and N. Cristianini, Margin distribution and soft margin, in Advances in Large Margin Classifiers, A.J. Smola et al., eds., The MIT Press, Cambridge, MA, 2000, pp. 349–358.
   Google Scholar
• O.G. Troyanskaya, M.E. Garber, P.O. Brown, D. Botstein, and R.B. Altman, Nonparametric methods for identifying differentially expressed genes in microarray data, Bioinformatics 18 (2002), pp. 1454–1461.
   PubMed Web of Science ®Google Scholar
• V. Vapnik and A. Lerner, Pattern recognition using generalized portrait method, Autom. Remote Control 24 (1963), pp. 774–780.
   Google Scholar
• V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
   Google Scholar
• V.N. Vapnik, Statistical Learning Theory, Wiley-Interscience, New York, 1998.
   Google Scholar
• L.J. Wei, The adaptive biased-coin design for sequential experiments, Ann. Stat. 6 (1978), pp. 92–100.
   Web of Science ®Google Scholar
• K. Yu, Q. Sang, P. Lung, W. Tan, T. Lively, C. Sherrield, M. Dargham, J. Liu, and J. Zhang, Personalized chemotherapy selection for breast cancer using gene expression profiles. Sci. Rep. 7 (2017), 43294.
   PubMed Web of Science ®Google Scholar
• H. Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67 (2005), pp. 301–320.
   Web of Science ®Google Scholar