Sep 282016
 

Common Statistical Methods for Clinical Research with SAS Examples, Third Edition


Features: Used Book in Good Condition
By (author): Glenn Walker Ph.D., Jack Shostak

Glenn Walker and Jack Shostak’s Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is a thoroughly updated edition of the popular introductory statistics book for clinical researchers. This new edition has been extensively updated to include the use of ODS graphics in numerous examples as well as a new emphasis on PROC MIXED. Straightforward and easy to use as either a text or a reference, the book is full of practical examples from clinical research to illustrate both statistical and SAS methodology. Each example is worked out completely, step by step, from the raw data. Common Statistical Methods for Clinical Research with SAS Examples, Third Edition, is an applications book with minimal theory. Each section begins with an overview helpful to nonstatisticians and then drills down into details that will be valuable to statistical analysts and programmers. Further details, as well as bonus information and a guide to further reading, are presented in the extensive appendices. This text is a one-source guide for statisticians that documents the use of the tests used most often in clinical research, with assumptions, details, and some tricks–all in one place.
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Sep 282016
 

Controversial Statistical Issues in Clinical Trials (Chapman & Hall/CRC Biostatistics Series)


By (author): Shein-Chung Chow

In clinical trial practice, controversial statistical issues inevitably occur regardless of the compliance with good statistical practice and good clinical practice. But by identifying the causes of the issues and correcting them, the study objectives of clinical trials can be better achieved. Controversial Statistical Issues in Clinical Trials covers commonly encountered controversial statistical issues in clinical trials and, whenever possible, makes recommendations to resolve these problems.

The book focuses on issues occurring at various stages of clinical research and development, including early-phase clinical development (such as bioavailability/bioequivalence), bench-to-bedside translational research, and late-phase clinical development. Numerous examples illustrate the impact of these issues on the evaluation of the safety and efficacy of the test treatment under investigation. The author also offers recommendations regarding possible resolutions of the problems.

Written by one of the preeminent experts in the field, this book provides a useful desk reference and state-of-the art examination of problematic issues in clinical trials for scientists in the pharmaceutical industry, medical/statistical reviewers in government regulatory agencies, and researchers and students in academia.

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Sep 092016
 

Clinician’s Guide to Medical Writing


By (author): Robert B. Taylor

This book is for any clinician who wants to write. It is for the physician, physician assistant, or nurse practitioner who sees patients and also wants to contribute to the medical literature. It is for the assistant professor aspiring to promotion and the clinician in private practice seeking personal enrichment. Loaded with practical advice and real-world examples, this text benefits readers who are new to medical writing and those who have authored a few articles or chapters and want to improve. Readers relate to this book because it is written by someone who has been in their shoes. Dr. Robert B. Taylor is a leader in the field of family medicine. Unlike the authors of many other books who have little experience outside of academia or publishing, writing is just one component of his career. He wrote this book to share what works and what doesn’t in medical writing. Clinicians learn how to translate observations and ideas from their practices into written form and eventually into print.
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Aug 302016
 

Basic Statistics and Epidemiology: A Practical Guide, Fourth Edition


By (author): Antony Stewart

Basic Statistics and Epidemiology is a straightforward primer in basic statistics that emphasizes its practical use in epidemiology and public health, providing an understanding of essential topics such as study design, data analysis and statistical methods used in the execution of medical research.

Assuming no prior knowledge, the clarity of the text and care of presentation ensure those new to, or challenged by, these topics are given a thorough introduction without being overwhelmed by unnecessary detail. An understanding and appreciation of statistics is central to ensuring that professional practice is based on the best available evidence, in order to treat and help most appropriately the wider community. By reading this book, students, researchers, doctors, nurses and health managers will have the knowledge necessary to understand and apply the tools of statistics and epidemiology to their own practice.

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Aug 182016
 

Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems (Excel for Statistics)


By (author): Thomas J Quirk, Meghan H. Quirk, Howard F. Horton

This book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science 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 is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Biological and Life Sciences 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 2016 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 biological and life science 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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Aug 172016
 

Using Statistics in the Social and Health Sciences with SPSS and Excel


By (author): Martin Lee Abbott

Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications

This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field.

The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material.

Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes:

• Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings

• Inclusion of a data lab section in each chapter that provides relevant, clear examples

• Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs

Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data.

Martin Lee Abbott, PhD, is Professor of Sociology at Seattle Pacific University, where he has served as Executive Director of the Washington School Research Center, an independent research and data analysis center funded by the Bill & Melinda Gates Foundation. Dr. Abbott has held positions in both academia and industry, focusing his consulting and teaching in the areas of statistical procedures, program evaluation, applied sociology, and research methods. He is the author of Understanding Educational Statistics Using Microsoft Excel® and SPSS®, The Program Evaluation Prism: Using Statistical Methods to Discover Patterns, and Understanding and Applying Research Design, also from Wiley.

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Aug 152016
 

A Biostatistics Toolbox for Data Analysis


By (author): Steve Selvin

This sophisticated package of statistical methods is for advanced master’s (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. It makes the link from statistical theory to data analysis, focusing on the methods and data types most common in public health and related fields. Like most toolboxes, the statistical tools in this book are organized into sections with similar objectives. Unlike most toolboxes, however, these tools are accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls – conveying skills, intuition, and experience. The only prerequisite is a first-year statistics course and familiarity with a computing package such as R, Stata, SPSS, or SAS. Though the book is not tied to a particular computing language, its figures and analyses were all created using R. Relevant R code, data sets, and links to public data sets are available from www.cambridge.org/9781107113084.
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Aug 132016
 

Prior Processes and Their Applications: Nonparametric Bayesian Estimation (Springer Series in Statistics)


By (author): Eswar G. Phadia

This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form.

However, the current interest in modeling and treating large-scale and complex data also poses a problem – the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own. 

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Aug 112016
 

Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases


The contributions by epidemic modeling experts describe how mathematical models and statistical forecasting are created to capture the most important aspects of an emerging epidemic.Readers will discover a broad range of approaches to address questions, such as

  • Can we control Ebola via ring vaccination strategies?
  • How quickly should we detect Ebola cases to ensure epidemic control?
  •  What is the likelihood that an Ebola epidemic in West Africa leads to secondary outbreaks in other parts of the world?  
  • When does it matter to incorporate the role of disease-induced mortality on epidemic models?
  •  What is the role of behavior changes on Ebola dynamics? 
  • How can we better understand the control of cholera or Ebola using optimal control theory?
  • How should a population be structured in order to mimic the transmission dynamics of diseases such as chlamydia, Ebola, or cholera?
  • How can we objectively determine the end of an epidemic?
  • How can we use metapopulation models to understand the role of movement restrictions and migration patterns on the spread of infectious diseases?
  • How can we capture the impact of household transmission using compartmental epidemic models?
  • How could behavior-dependent vaccination affect the dynamical outcomes of epidemic models? 

The derivation and analysis of the mathematical models addressing these questions provides a wide-ranging overview of the new approaches being created to better forecast and mitigate emerging epidemics. 

This book will be of interest to researchers in the field of mathematical epidemiology, as well as public health workers.

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Aug 032016
 

Measurement and Analysis in Transforming Healthcare Delivery: Volume 1: Quantitative Approaches in Health Systems Engineering


By (author): Peter J. Fabri

This volume provides the important concepts necessary for a physician to participate in a reengineering process, develop decision-making skills based on probability and logic rather than “rules,” and to measure and analyze meaningful outcomes of care delivery.  This approach has been developed over ten years in a medical student-based program and has been enthusiastically embraced by medical students without backgrounds in engineering or statistics.  More specifically, this text will introduce physicians to relevant and available computer software, combined with an in depth knowledge of measurement, variation, and uncertainty.  It provides a basis for the transformation of data into information, information into knowledge, and knowledge into wisdom.  The first quarter of the book will address understanding and visualizing data, using statistical and graphic analysis.  The next quarter addresses the fundamentals of applied statistics, and the application of conditional probability to clinical decision making.  The next quarter addresses the four “cornerstones” of modern analytics: regression, classification, association analysis, and clustering.  The final section addresses the identification of outliers and their importance in understanding, the assessment of cause and effect and the limitations associated with retrospective data analysis. This toolbox will prepare the interested physician to actively engage in the identification of problem areas, the design of process-based solutions, and the continuous assessment of outcomes of clinical practice.  Armed with this toolbox, the reader will be “prepared to make a difference” in the rapidly changing world of healthcare delivery.
Measurement and Analysis in Transforming Healthcare Delivery is an excellent resource for general practitioners, health administrators, and all medical professionals interacting with healthcare delivery.
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