This unique volume focuses on the “tools” of medical statistics. It contains over 500 concepts or methods, all of which are explained very clearly and in detail.
Each chapter focuses on a specific field and its applications. There are about 20 items in each chapter with each item independent of one another and explained within one page (plus references). The structure of the book makes it extremely handy for solving targeted problems in this area.
As the goal of the book is to encourage students to learn more combinatorics, every effort has been made to provide them with a not only useful, but also enjoyable and engaging reading.
This handbook plays the role of “tutor” or “advisor” for teaching and further learning. It can also be a useful source for “MOOC-style teaching”.
Readership: Biostatisticians, applied statisticians, medical researchers and clinicians, bioscience students, biopharmaceutical researchers, public health epidemiologists; biometricians& applied mathematicians.
Features: LWW By (author): Anthony N. Glaser MD Ph.D
High-Yield™ Biostatistics, Epidemiology, and Public Health, Fourth Edition provides a concise review of the biostatistics concepts that are tested in the USMLE Step 1. Information is presented in an easy-to-follow format, with High-Yield Points that help students focus on the most important USMLE Step 1 facts. The High-Yield™ outline format, with tables, diagrams, photographs, and images to clarify important material, provides a concentrated, efficient review for both course exams and the USMLE.
Basic Biostatistics is a concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. The text puts equal emphasis on exploratory and confirmatory statistical methods. Sampling, exploratory data analysis, estimation, hypothesis testing, and power and precision are covered through detailed, illustrative examples.
The book is organized into three parts: Part I addresses basic concepts and techniques; Part II covers analytic techniques for quantitative response variables; and Part III covers techniques for categorical responses.
The Second Edition offers many new exercises as well as an all new chapter on “Poisson Random Variables and the Analysis of Rates.”
With language, examples, and exercises that are accessible to students with modest mathematical backgrounds, this is the perfect introductory biostatistics text for undergraduates and graduates in various fields of public health.
Illustrative, relevant examples and exercises incorporated throughout the book. Answers to odd-numbered exercises provided in the back of the book. (Instructors may requests answers to even-numbered exercises from the publisher. Chapters are intentionally brief and limited in scope to allow for flexibility in the order of coverage. Equal attention is given to manual calculations as well as the use of statistical software such as StaTable, SPSS, and WinPepi. Comprehensive Companion Website with Student and Instructor’s Resources.
By (author): Amir Momeni, Matthew Pincus, Jenny Libien
This text provides a comprehensive and practical review of the main statistical methods in pathology and laboratory medicine. It introduces statistical concepts used in pathology and laboratory medicine. The information provided is relevant to pathologists both for their day to day clinical practice as well as in their research and scholarly activities. The text will begins by explaining the fundamentals concepts in statistics. In the later sections, these fundamental concepts are expanded and unique applications of statistical methods in pathology and laboratory medicine practice are introduced. Other sections of the text explain research methodology in pathology covering a broad range of topics from study design to analysis of data. Finally, data-heavy novel concepts that are emerging in pathology and pathology research are presented such as molecular pathology and pathology informatics.
Introduction to Statistical Methods in Pathology will be of great value for pathologists, pathology residents, basic and translational researchers, laboratory managers and medical students.
By (author): Katsumi Kobayashi, K. Sadasivan Pillai
Statistics plays an important role in pharmacology and related subjects such as toxicology and drug discovery and development. Improper statistical tool selection for analyzing the data obtained from studies may result in wrongful interpretation of the performance or safety of drugs. This book communicates statistical tools in simple language. The examples used are similar to those that scientists encounter regularly in their research area. The authors provide cognitive clues for selection of appropriate tools to analyze the data obtained from the studies and explain how to interpret the result of the statistical analysis.
Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches.
The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.
Features: Health and Lifestyle Separating the Truth from the Myth with Statistics By (author): Brian S. Everitt
The main message of this book is that people should be on their guard against both scare stories about risks to health, and claims for miracle cures of medical conditions. In the 21st century hardly a day passes without another article appearing in the media about a new treatment for a particular disease, new ways of improving our health by changing our lifestyle or new foodstuffs that claim to increase (or decrease) the risk of heart disease, cancer and the like. But how should the general public react to such claims, given that some of the journalists writing them focus on the sensational rather than the mundane and often have no qualms about sacrificing accuracy and honesty for the sake of a good story? Perhaps the wisest initial response is one of healthy scepticism, followed by an attempt to discover more about the details of the studies behind the reports. But most people are not, and have little desire to become experts in health research. By reading this book, however, these non-experts can, with minimal effort, learn enough about the scientific method to differentiate between those health claims, warnings and lifestyle recommendations that have some merit and those that are unproven or simply dishonest. So if you want to know if ginseng can really help with your erectile dysfunction, if breast cancer screening is all that politicians claim it to be, if ECT for depression is really a horror treatment and should be banned, if using a mobile phone can lead to brain tumours and how to properly evaluate the evidence from health and lifestyle related studies, then this is the book for you.
By (author): Ding-Geng (Din) Chen, Karl E. Peace, Pinggao Zhang
Review of the First Edition
“The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it …The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods.” ?Journal of Statistical Software
Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data.
What’s New in the Second Edition
Adds SAS programs along with the R programs for clinical trial data analysis.
Updates all the statistical analysis with updated R packages.
Includes correlated data analysis with multivariate analysis of variance.
Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials.
Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.