Renowned experts in the neuro-oncological field bring their expertise together for Neuro-Oncology Compendium for the Boards and Clinical Practice. This volume reviews the core topics of neuro-oncology including adult and pediatric neuro-oncology, management, central nervous system tumor complications, genetic considerations, and more. With a focus on updated treatments and terminology, this volume is designed to comprehensively review all major facets of neuro-oncology so that physicians-in-training may prepare for the board review and practicing specialists can stay up to date in their treatment of patients. This essential text includes hundreds of figures and tables, succinct review flashcards, end-of-chapter questions and answers, as well as end-of-volume exams so readers can review and test their own comprehension. Each chapter has been reviewed by the editors to ensure cohesive board-level verbiage, emphasizing practical clinical knowledge. Neuro-Oncology Compendium for the Boards and Clinical Practice is up-to-date and comprehensive, eliminating the need for multiple sources of study. Key features of this volume include: -End of chapter flashcards to summarize key content -End of chapter Questions and Answers for review -Practice exams to simulate board questions -Updated terminology and practices -Complimentary images and tables to support learning

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.