Dec 202016

Medical Statistics from Scratch: An Introduction for Health Professionals

Features: Wiley-Interscience

Correctly understanding and using medical statistics is a key skill for all medical students and health professionals.

In an informal and friendly style, Medical Statistics from Scratch provides a practical foundation for everyone whose first interest is probably not medical statistics. Keeping the level of mathematics to a minimum, it clearly illustrates statistical concepts and practice with numerous real world examples and cases drawn from current medical literature.

This fully revised and updated third edition includes new material on: 

  • missing data, random allocation and concealment of data
  • intra-class correlation coefficient
  • effect modification and interaction
  • diagnostic testing and the ROC curve
  • standardisation

Medical Statistics from Scratch is an ideal learning partner for all medical students and health professionals needing an accessible introduction, or a friendly refresher, to the fundamentals of medical statistics.

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Dec 192016

Clinical Trials with Missing Data: A Guide for Practitioners (Statistics in Practice)

By (author): Michael O’Kelly, Bohdana Ratitch

This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable.

The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively.

The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included – the reader is given a toolbox for implementing analyses under a variety of assumptions.

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Dec 162016

Statistical Methods for Healthcare Performance Monitoring (Chapman & Hall/CRC Biostatistics Series)

By (author): Alex Bottle, Paul Aylin

Healthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions.

Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists.

Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.

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Dec 162016

Statistical Testing Strategies in the Health Sciences (Chapman & Hall/CRC Biostatistics Series)

By (author): Albert Vexler, Alan D. Hutson, Xiwei Chen

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments.

The book is organized primarily based on the type of questions to be answered by inference procedures or according to the general type of mathematical derivation. It establishes the theoretical framework for each method, with a substantial amount of chapter notes included for additional reference. It then focuses on the practical application for each concept, providing real-world examples that can be easily implemented using corresponding statistical software code in R and SAS. The book also explains the basic elements and methods for constructing correct and powerful statistical decision-making processes to be adapted for complex statistical applications.

With techniques spanning robust statistical methods to more computationally intensive approaches, this book shows how to apply correct and efficient testing mechanisms to various problems encountered in medical and epidemiological studies, including clinical trials. Theoretical statisticians, medical researchers, and other practitioners in epidemiology and clinical research will appreciate the book’s novel theoretical and applied results. The book is also suitable for graduate students in biostatistics, epidemiology, health-related sciences, and areas pertaining to formal decision-making mechanisms.

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Dec 062016

Big Data Analytics in Genomics

This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace.  To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.
This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA.  In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science.  Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
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Dec 062016

Biodemography of Aging: Determinants of Healthy Life Span and Longevity (The Springer Series on Demographic Methods and Population Analysis)

By (author): Anatoliy I. Yashin, Eric Stallard, Kenneth C. Land

This volume is a critical exposition of the data and analyses from a full decade of rigorous research into how age-related changes at the individual level, along with other factors, contribute to morbidity, disability and mortality risks at the broader population level. After summarizing the state of our knowledge in the field, individual chapters offer enlightening discussion on a range of key topics such as age trajectory analysis in select and general populations, incidence/age patterns of major chronic illnesses, and indices of cumulative deficits and their use in characterizing and understanding the detailed properties of individual aging.
The book features comprehensive statistical analyses of unique longitudinal data sets including the unique resource of the Framingham Heart Study, with its more than 60 years of follow-up. Culminating in penetrating conclusions about the insights gained from the work involved, this book adds much to our understanding of the links between aging and human health.
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Nov 282016

The Multiple Facets of Partial Least Squares and Related Methods: PLS, Paris, France, 2014 (Springer Proceedings in Mathematics & Statistics)

This volume presents state of the art theories, new developments, and important applications of Partial Least Square (PLS) methods. The text begins with the invited communications of current leaders in the field who cover the history of PLS, an overview of methodological issues, and recent advances in regression and multi-block approaches. The rest of the volume comprises selected, reviewed contributions from the 8th International Conference on Partial Least Squares and Related Methods held in Paris, France, on 26-28 May, 2014. They are organized in four coherent sections: 1) new developments in genomics and brain imaging, 2) new and alternative methods for multi-table and path analysis, 3) advances in partial least square regression (PLSR), and  4) partial least square path modeling (PLS-PM) breakthroughs and applications. PLS methods are very versatile methods that are now used in areas as diverse as engineering, life science, sociology, psychology, brain imaging, genomics, and business among both academics and practitioners. The selected chapters here highlight this diversity with applied examples as well as the most recent advances.
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Nov 282016

Excel 2016 for Health Services Management Statistics: A Guide to Solving Problems (Excel for Statistics)

By (author): Thomas J. Quirk, Simone M. Cummings

This book shows the capabilities of Microsoft Excel in teaching health services management statistics effectively. Similar to the previously published Excel 2013 for Health Services Management Statistics, this book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical health service management problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.

Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in health service courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Health Services Management Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.

Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand health service management problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

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Nov 262016

Computational Methods for Three-Dimensional Microscopy Reconstruction (Applied and Numerical Harmonic Analysis)

Approaches to the recovery of three-dimensional information on a biological object, which are often formulated or implemented initially in an intuitive way, are concisely described here based on physical models of the object and the image-formation process. Both three-dimensional electron microscopy and X-ray tomography can be captured in the same mathematical framework, leading to closely-related computational approaches, but the methodologies differ in detail and hence pose different challenges. The editors of this volume, Gabor T. Herman and Joachim Frank, are experts in the respective methodologies and present research at the forefront of biological imaging and structural biology.

Computational Methods for Three-Dimensional Microscopy Reconstruction will serve as a useful resource for scholars interested in the development of computational methods for structural biology and cell biology, particularly in the area of 3D imaging and modeling.

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Nov 252016

Introduction to Statistics for Forensic Scientists

By (author): David Lucy

Introduction to Statistics for Forensic Scientists is an essential introduction to the subject, gently guiding the reader through the key statistical techniques used to evaluate various types of forensic evidence. Assuming only a modest mathematical background, the book uses real-life examples from the forensic science literature and forensic case-work to illustrate relevant statistical concepts and methods.

Opening with a brief overview of the history and use of statistics within forensic science, the text then goes on to introduce statistical techniques commonly used to examine data obtained during laboratory experiments. There is a strong emphasis on the evaluation of scientific observation as evidence and modern Bayesian approaches to interpreting forensic data for the courts. The analysis of key forms of evidence are discussed throughout with a particular focus on DNA, fibres and glass.

An invaluable introduction to the statistical interpretation of forensic evidence; this book will be invaluable for all undergraduates taking courses in forensic science.

  • Introduction to the key statistical techniques used in the evaluation of forensic evidence
  • Includes end of chapter exercises to enhance student understanding
  • Numerous examples taken from forensic science to put the subject into context
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