Features: Routledge By (author): Vicent Montalt, Maria González-Davies
Statistics on the translation market consistently identify medicine as a major thematic area as far as volume or translation is concerned. Vicent Montalt and Maria Gonzalez Davis, both experienced translator trainers at Spanish universities, explain the basics of medical translation and ways of teaching and learning how to translate medical texts.
Medical Translation Step by Step provides a pedagogical approach to medical translation based on learner and learning-centred teaching tasks, revolving around interaction: pair and group work to carry out the tasks and exercises to practice the points covered. These include work on declarative and operative knowledge of both translation and medical texts and favour an approach that takes into account both the process and product of translations. Starting from a broad communication framework, the book follows a top-down approach to medical translation: communication ? genres ? texts ? terms and other units of specialized knowledge. It is positively focused in that it does not insist on error analysis, but rather on ways of writing good translations and empowering both students and teachers.
The text can be used as a course book for students in face-to-face learning, but also in distance and mixed learning situations. It will also be useful for teachers as a resource book, or a core book to be complemented with other materials.
Features: Used Book in Good Condition By (author): Ding-Geng (Din) Chen, Karl E. Peace
Too often in biostatistical research and clinical trials, a knowledge gap exists between developed statistical methods and the applications of these methods. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book’s practical, detailed approach draws on the authors’ 30 years of real-world experience in biostatistical research and clinical development.
Each chapter presents examples of clinical trials based on the authors’ actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data.
With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.
Features: Used Book in Good Condition By (author): Donald W. Black. M.D., Jon E. Grant, M.D.
As a companion to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5[trademark]), the DSM-5[trademark] Guidebook serves two critical functions. First, it acts as a guide for busy clinicians in need of practical information on the use of diagnostic criteria and codes, documentation, and compensation. Second, it serves as an educational text and includes a structured curriculum that facilitates its use in courses and workshops. The guidebook demystifies DSM-5[trademark] and makes the content more accessible. The publication of DSM-5[trademark] has an enormous impact on every mental health professional, but especially clinicians, who need to know how to implement the diagnostic classification in their practices. The guidebook provides an entry point for clinicians, covering everything from coding changes to specific diagnoses to dimensional assessments. Practical and focused the DSM-5[trademark] Guidebook deserves its place next to DSM-5[trademark] in every clinician’s office.
By (author): Ton J. Cleophas, Aeilko H. Zwinderman
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.
In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.
Providing an interface between dry-bench bioinformaticians and wet-lab biologists, DNA Methylation Microarrays: Experimental Design and Statistical Analysis presents the statistical methods and tools to analyze high-throughput epigenomic data, in particular, DNA methylation microarray data. Since these microarrays share the same underlying principles as gene expression microarrays, many of the analyses in the text also apply to microarray-based gene expression and histone modification (ChIP-on-chip) studies.
After introducing basic statistics, the book describes wet-bench technologies that produce the data for analysis and explains how to preprocess the data to remove systematic artifacts resulting from measurement imperfections. It then explores differential methylation and genomic tiling arrays. Focusing on exploratory data analysis, the next several chapters show how cluster and network analyses can link the functions and roles of unannotated DNA elements with known ones. The book concludes by surveying the open source software (R and Bioconductor), public databases, and other online resources available for microarray research.
Requiring only limited knowledge of statistics and programming, this book helps readers gain a solid understanding of the methodological foundations of DNA microarray analysis.
Features: Used Book in Good Condition By (author): Glenn A. Walker, 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.
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.
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.