Data Mining and Statistically Guided Clinical Review of Adverse Event Data in Clinical Trials

REFERENCES

• Berry , S. M. , Berry , D. A. ( 2004 ). Accounting for multiplicities in assessing drug safety: A three-level hierarchical mixture model . Biometrics 60 : 418 – 426 .
   PubMed Web of Science ®Google Scholar
• Breiman , L. ( 1996 ). Bagging predictors . Machine Learning 24 : 123 – 140 .
   Web of Science ®Google Scholar
• Breiman , L. ( 2001 ). Random forests . Machine Learning 45 : 5 – 32 .
   Web of Science ®Google Scholar
• Carroll , K. ( 2007 ). Analysis of progression-free survival in oncology trials: Some common statistical issues . Pharm. Stat. 6 : 99 – 113 .
   PubMed Web of Science ®Google Scholar
• Chen , C. , Liaw , A. , Breiman , L. ( 2004 ). Using Random Forest to Learn Imbalanced Data. Technical report, University of Calafornia at Berkley, Statistics Department .
   Google Scholar
• Committee for Proprietary Medicinal Products. ( 2002 ). Points to Consider on Multiplicity Issues in Clinical Trials . London : Taylor & Francis .
   Google Scholar
• Council for International Organizations of Medical Sciences Working Group VI ( 2005 ). Management of Safety Information from Clinical Trials . London : Taylor & Francis .
   Google Scholar
• Cox , D. R. ( 2006 ). Principles of Statistical Inference . Cambridge : Cambridge University Press .
   Google Scholar
• Cuzick , J. ( 1982 ). The efficiency of the proprotions test and the log rank test for censored survival data . Biometrics 38 : 1033 – 1039 .
   Web of Science ®Google Scholar
• Edwards , A. W. F. ( 1992 ). Likelihood (expanded ed.) Baltimore , MD : John Hopkins University Press .
   Google Scholar
• Efron , B. , Hastie , T. , Johnstone , I. , Tibshirani , R. ( 2004 ). Least angle regression . Ann. Stat. 32 : 407 – 499 .
   Web of Science ®Google Scholar
• Fidler , F. , Geoff , C. , Mark , B. , Neil , T. ( 2004 ). Statistical reform in medicine, psychology and ecology . J. Socio-Econ. 33 : 615 – 630 .
   Google Scholar
• Firth , D. ( 1993 ). Bias reduction of maximum likelihood estimates . Biometrika 1980 : 27 – 38 .
   Google Scholar
• Fisher , R. A. ( 1973 ). Statistical Methods and Scientific Inference. , 3rd ed. London : Hafner Press .
   Google Scholar
• Friedman , J. H. ( 2001 ). Greedy function approximation: A gradient boosting machine . Ann. Stat. 29 ( 5 ): 1189 – 1232 .
   Web of Science ®Google Scholar
• Gail , M. H. ( 1985 ). Applicability of sample size calculations based on a comparison of proportions for use with the log rank test . Control. Clin. Trials 6 : 112 – 119 .
   PubMedGoogle Scholar
• Hand , D. , Mannila , H. , Smyth , P. ( 2001 ). Principles of Data Mining . Cambridge , MA : MIT Press .
   Google Scholar
• Hastie , T. , Tibshirani , R. , Friedman , J. ( 2001 ). The Elements of Statistical Learning . London : Springer .
   Google Scholar
• Heinze , G. , Schemper , M. ( 2001 ). A solution to the problem of monotone likelihood in cox regression . Biometrics 57 : 114 – 119 .
   PubMed Web of Science ®Google Scholar
• Heinze , G. , Schemper , M. ( 2002 ). A solution to the problem of monotone likelihood in logistic regression . Stat. Med. 21 : 2409 – 2419 .
   PubMed Web of Science ®Google Scholar
• International Conference on Harmonisation. ( 1998 ). Statistical Principles for Clinical Trials . London : Taylor & Francis .
   Google Scholar
• Jeffreys , H. ( 1939 ). Theory of Probability . Oxford : Oxford University Press .
   Google Scholar
• Kass , R. E. , Raftery , A. E. (1995). Bayes factors. J. Am. Stat. Assoc. 90:188–234.
   Web of Science ®Google Scholar
• Krantz , D. H. ( 1999 ). The null hypothesis testing controversy in psychology . J. Am. Stat. Assoc. 44 : 1373 – 1381 .
   Google Scholar
• Lee , Y. , Nelder , J. A. , Pawitan , Y. ( 2006 ). Generalized Linear Models With Random Effects . Boca Raton , FL : Chapman and Hall/CRC .
   Google Scholar
• Lindsey , J. K. ( 1996 ). Parametric Statistical Inference . Oxford : Oxford University Press .
   Google Scholar
• Longford , N. T. , Nelder , J. A. ( 1999 ). Statistics versus statistical science in the regulatory process . Stat. Med. 18 : 2311 – 2320 .
   PubMed Web of Science ®Google Scholar
• Nelder , J. A. ( 1999 ). Statistics for the millenium: From statistics to statistical science . Statistician 43 : 257 – 269 .
   Google Scholar
• Nester , M. R. ( 1996 ). An applied statistician's creed . Appl. Stat. 45 : 401 – 410 .
   Web of Science ®Google Scholar
• O’Brien , P. C. ( 1988 ). Comparing two samples: extensions of the t, rank-sum, and log-rank test . J. Am. Stat. Assoc. 83 : 52 – 61 .
   Web of Science ®Google Scholar
• Ripley , B. D. ( 1996 ). Pattern Recognition and Neural Networks . Cambridge : Cambridge University Press .
   Google Scholar
• Royall , R. ( 1997 ). Statistical Evidence: A Likelihood Paradigm . Boca Raton , FL : Chapman and Hall/CRC .
   Google Scholar
• Tibshirani , R. ( 1996 ). Regression shrinkage and selection via the lasso . J. R. Stat. Soc. Ser. B 58 : 267 – 288 .
   Google Scholar