"Genomic Clinical Trials and Predictive Medicine" by Richard M. Simon is a timely book that explores the role of genomic data in clinical trial design and analysis. It focuses on how genomic data can be used to personalize treatment and improve clinical trial outcomes. The book is aimed at statisticians, clinical investigators, and translational scientists. It covers clinical trial design, the development of diagnostic tests using biomarkers, and the use of genomic data in phase II and III trials. The book includes practical examples and is heavily oriented towards oncology, though the statistical concepts are broadly applicable. The first chapter provides an introduction to clinical trials, including phase I, II, and III trials, power calculations, and interim analyses. The second chapter discusses actionable prognostic biomarkers, highlighting the importance of identifying patients who may benefit from lower intensity therapy. The book also covers phase II trials and the role of genomic markers in these trials, including Bayesian adaptive designs. Chapters 4 and 5 discuss the merits of different trial designs for molecularly targeted agents. Chapter 7 deals with developing and validating predictive markers in randomized trials, while the final chapter provides an overview of the "Prospective-Retrospective" design. The book is a valuable resource for those involved in genomic clinical trials, offering a clear and practical approach to handling these issues. "Statistical Learning with Sparsity: The Lasso and Generalizations" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is a comprehensive text on sparse statistical modeling, focusing on the Lasso and its generalizations. The book is aimed at statistics graduate and advanced undergraduate students, as well as practitioners in the biological and physical sciences. It provides a well-written and authoritative overview of sparse statistical models, emphasizing the importance of using only a small number of non-zero parameters. The book is accessible to readers with a basic knowledge of linear algebra, least-squares, and numerical analysis. It includes exercises and applications to reinforce the theory, making it a valuable resource for both theoreticians and practitioners. The book discusses the importance of sparsity in the context of Big Data and predicts that this area will continue to develop as data collection becomes more cost-effective. "Bayesian Nonparametric Data Analysis" by Peter Müller, Fernando Andres Quintana, Alejandro Jara, and Tim Hanson is a well-structured and comprehensive text on Bayesian nonparametric methods. The book covers a wide range of non-parametric Bayesian models and methods, including Dirichlet processes, Pólya trees, wavelet-based models, and neural network models. It is aimed at individuals with an understanding of Bayesian principles and those interested in non-parametric applications. The book is well-written and structured, with a clear focus on data analysis. It includes a wealth of examples and applications, making it a valuable reference for statisticians interested in Bayesian non-parametric data analysis. "Current Trends in Bayesian Methodology with Applications" by Satyanshu K."Genomic Clinical Trials and Predictive Medicine" by Richard M. Simon is a timely book that explores the role of genomic data in clinical trial design and analysis. It focuses on how genomic data can be used to personalize treatment and improve clinical trial outcomes. The book is aimed at statisticians, clinical investigators, and translational scientists. It covers clinical trial design, the development of diagnostic tests using biomarkers, and the use of genomic data in phase II and III trials. The book includes practical examples and is heavily oriented towards oncology, though the statistical concepts are broadly applicable. The first chapter provides an introduction to clinical trials, including phase I, II, and III trials, power calculations, and interim analyses. The second chapter discusses actionable prognostic biomarkers, highlighting the importance of identifying patients who may benefit from lower intensity therapy. The book also covers phase II trials and the role of genomic markers in these trials, including Bayesian adaptive designs. Chapters 4 and 5 discuss the merits of different trial designs for molecularly targeted agents. Chapter 7 deals with developing and validating predictive markers in randomized trials, while the final chapter provides an overview of the "Prospective-Retrospective" design. The book is a valuable resource for those involved in genomic clinical trials, offering a clear and practical approach to handling these issues. "Statistical Learning with Sparsity: The Lasso and Generalizations" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is a comprehensive text on sparse statistical modeling, focusing on the Lasso and its generalizations. The book is aimed at statistics graduate and advanced undergraduate students, as well as practitioners in the biological and physical sciences. It provides a well-written and authoritative overview of sparse statistical models, emphasizing the importance of using only a small number of non-zero parameters. The book is accessible to readers with a basic knowledge of linear algebra, least-squares, and numerical analysis. It includes exercises and applications to reinforce the theory, making it a valuable resource for both theoreticians and practitioners. The book discusses the importance of sparsity in the context of Big Data and predicts that this area will continue to develop as data collection becomes more cost-effective. "Bayesian Nonparametric Data Analysis" by Peter Müller, Fernando Andres Quintana, Alejandro Jara, and Tim Hanson is a well-structured and comprehensive text on Bayesian nonparametric methods. The book covers a wide range of non-parametric Bayesian models and methods, including Dirichlet processes, Pólya trees, wavelet-based models, and neural network models. It is aimed at individuals with an understanding of Bayesian principles and those interested in non-parametric applications. The book is well-written and structured, with a clear focus on data analysis. It includes a wealth of examples and applications, making it a valuable reference for statisticians interested in Bayesian non-parametric data analysis. "Current Trends in Bayesian Methodology with Applications" by Satyanshu K.