Practical Biostatistics

There is an ever-increasing emphasis on evidence-based medicine that is distinguished by systematic crafting of the patient-centered research question, detailed literature searches, and careful apprai

Evidence-based medicine aims to apply the best available evidence gained from the scientific method to medical decision making. It is a practice that uses statistical analysis of scientific methods an

Analyze your biostatistics data with JMP! Trevor Bihl's Biostatistics Using JMP: A Practical Guide provides a practical introduction on using JMP, the interactive statistical discovery software, to so

Encyclopedic in breadth, yet practical and concise, Medical Biostatistics, Fourth Edition focuses on the statistical aspects ofmedicine with a medical perspective, showing the utility of biostatistics

Anyone who attempts to read genetics or epidemiology research literature needs to understand the essentials of biostatistics. This book, a revised new edition of the successful Essentials of Biostatis

Anyone who attempts to read genetics or epidemiology research literature needs to understand the essentials of biostatistics. This book, a revised new edition of the successful Essentials of Biostatis

Biostatistics for Oncologists is the first practical guide providing the essential biostatistical concepts, oncology-specific examples, and applicable problem sets for medical oncologists, radiation o

This popular and critically acclaimed text for undergraduates concentrates on the practical applications of statistics to biology. Its offers sufficient detail to be coherent to students with a minima

Encyclopedic Dictionary of Biostatistics for Medical Professionals focuses on conceptual knowledge and practical advice rather than mathematical details, enhancing its usefulness as a reference for me

This book introduces and discusses the most important aspects of clinical research methods and biostatistics for oncologists, pursuing a tailor-made and practical approach. Evidence-based medicine (EB

A practical and clarifying approach to aging and aging-related diseasesProviding a thorough and extensive theoretical framework, The Biostatistics of Aging: From Gompertzian Mortality to an Index of A

The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a w

The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a w

Practical Biostatistics: A Step-by-Step Approach for Evidence-Based Medicine, Second Edition presents a complete resource of biostatistical knowledge meant for health sciences students, researchers an

A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and

Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care

Quantitative Research Methods for Health Professionals: A Practical Interactive Course is a superb introduction to epidemiology, biostatistics, and research methodology for the whole health care

This is a practical guide to the design, conduction, analysis and reporting of clinical trials with anticancer drugs. It includes coverage of basic biostatistics for the clinical trialist, and fundame

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts as bias, confounding, independence, and interaction are presented in the context of survival analysis and also are basic components of a broad range of applications. These topics make up essentially a 'second-year', one-semester biostatistics course in

Modern statistical methods use complex, sophisticated models that can lead to intractable computations. Saddlepoint approximations can be the answer. Written from the user's point of view, this book explains in clear language how such approximate probability computations are made, taking readers from the very beginnings to current applications. The core material is presented in chapters 1-6 at an elementary mathematical level. Chapters 7-9 then give a highly readable account of higher-order asymptotic inference. Later chapters address areas where saddlepoint methods have had substantial impact: multivariate testing, stochastic systems and applied probability, bootstrap implementation in the transform domain, and Bayesian computation and inference. No previous background in the area is required. Data examples from real applications demonstrate the practical value of the methods. Ideal for graduate students and researchers in statistics, biostatistics, electrical engineering, econometric

Compact and highly practical this step-by-step textbook of biostatistics gives students and practitioners specific answers about the statistical analysis needed for a specific clinical or basic resear