Design and Analysis of Clinical Trials for Predictive Medicine provides statistical guidance on conducting clinical trials for predictive medicine. It covers statistical topics relevant to the main clinical research phases for developing molecular diagnostics and therapeutics-from identifying molecular biomarkers using DNA microarrays to confirming

INTRODUCTORY OVERVIEW. Clinical Trials for Predictive Medicine: New Paradigms and Challenges. An Industry Statistician's Perspective on Personalized Health Care Drug Development. Analytical Validation of In Vitro Diagnostic Tests. EARLY CLINICAL TRIALS USING BIOMARKERS. Phase I Dose-Finding Designs and Their Applicability to Targeted Therapies. An Overview of Phase II Clinical Trial Designs with Biomarkers. Bayesian Adaptive Methods for Clinical Trials of Targeted Agents. Outcome-Adaptive Randomization in Early Clinical Trials. Challenges of Using Predictive Biomarkers in Clinical Trials. PHASE III RANDOMIZED CLINICAL TRIALS USING BIOMARKERS. Comparison of Randomized Clinical Trial Designs for Targeted Agents. Phase III All-Comers Clinical Trials with a Predictive Biomarker. Evaluation of Clinical Utility and Validation of Gene Signatures in Clinical Trials. ANALYSIS OF HIGH-DIMENSIONAL DATA AND GENOMIC SIGNATURE DEVELOPMENTS. Statistical Issues in Clinical Development and Validation of Genomic Signatures. Univariate Analysis for Gene Screening: Beyond Multiple Testing. Statistical and Machine-Learning Methods for Class Prediction in High Dimension. Survival Risk Prediction Using High-Dimensional Molecular Data. RANDOMIZED TRIALS WITH BIOMARKER DEVELOPMENT AND VALIDATION. Adaptive Clinical Trial Designs with Biomarker Development and Validation. Development and Validation of Continuous Genomic Signatures in Randomized Clinical Trials. EVALUATION OF SURROGATE BIOMARKERS. Biomarker-Based Surrogate Endpoints.