In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.

This book provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. The book covers state-of-the-art approaches to the design and analysis of cancer clinical trials, such as adaptive designs, biomarker-based trials, and dynamic treatment regimes.

The proposed book will focus on practical issues in pharmaceutical/clinical research and development from both regulatory and scientific perspectives. The book will outline and clarify those issues that have been commonly misused in the subject area of regulatory science.

This book is a concepts-based introduction to statistical procedures that prepares public health, medical, and life sciences students to conduct and evaluate research. Thoroughly revised, this third edition includes a new chapter introducing the basic principles of Study Design.

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the years as the need for a robust evidence base has become clear in many scientific areas like medicine and health, social sciences, education, psychology, ecology and economics.

This book concentrates on the biostatistics component of clinical trials. This new, second edition is updated throughout and includes three new chapters. This text reflects the academic research, commercial development, and public health aspects of clinical trials.

This book will be a vital reference for medical researchers, health technology innovators, data scientists, epidemiologists, population health analysts, health economists, outcomes researchers, as well as policymakers and analysts in the healthcare industry.

With over 60 years of applied experience, Fay and Brittain present hypothesis testing and compatible confidence intervals, emphasize strategies to address the reproducibility crisis, and provide methods for proper causal interpretation in scientific research. The book presents a full scope of tools and advice on their appropriate use in practice.

Using every-day examples and numerous exercises, this text covers the basics of linear models with a minimum of mathematics. The emphasis is on issues involved in the analysis and the interpretation of computer output. R code is provided and explained allowing readers to apply the methods to their own data.