Genomic Clinical Trials and Predictive Medicine
Genomics is majorly impacting therapeutics development in medicine. This book contains up-to-date information on the use of genomics in the design and analysis of therapeutic clinical trials with a focus on novel approaches that provide a reliable basis for identifying which patients are most likely to benefit from each treatment. It is oriented to both clinical investigators and statisticians. For clinical investigators, it includes background information on clinical trial design and statistical analysis. For statisticians and others who want to go deeper, it covers state-of-the-art adaptive designs and the development and validation of probabilistic classifiers. The author describes the development and validation of prognostic and predictive biomarkers and their integration into clinical trials that establish their clinical utility for informing treatment decisions for future patients.
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Including Both TestPositive and TestNegative Patients
Adaptive Threshold Design
Appendix B Prognostic Classiﬁers Based
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accrual algorithm analysis plan approach assay Bayesian beneﬁt binary breast cancer candidate biomarkers centroid chemotherapy class prediction computed conﬁdence covariates cross-validation cut point deﬁned denote disease drug end—point enrichment design erlotinib error rate evaluate ﬁt Freidlin gene expression genomic global null hypothesis hazard ratio identiﬁes indication classiﬁer Kaplan—Meier likelihood likelihood function linear discriminant analysis Low Risk means medical utility methods microarray null hypothesis number of candidate number of patients Oncology Oncotype Oncotype DX optimal outcome parameters permutation test phase II trials phase III clinical predictive biomarker predictive classiﬁer predictor prespeciﬁed prev principal components prior distributions proportional hazards model randomized patients reﬂect regression coefﬁcients risk groups ROC curve sample size planning signiﬁcance level signiﬁcance test Simon simulated speciﬁed statistical power statistical signiﬁcance subset sufﬁcient survival data test set test statistic test—negative patients test—positive patients threshold training set treatment effect tumor type I error validation set variables vector