This book deals with statistics in medicine in a simple way. The text is supported by abundant examples from medical data. This book aims to explain and simplify the process of data presentation. Further aspects addressed include how to design and conduct clinical trials, and how to write journal articles.
This unique publication explores diverse themes relating to thrombosis and embolism, from basic research at cell and molecular level to the actual care, prevention, and treatment of diverse categories of patients suffering from such diseases. Chapters cover a variety of topics including thrombosis and embolism in surgical patients, cancer patients, pregnant women and children and adolescents, as well as treatment of the conditions by traditional anticoagulants, novel oral anticoagulants, thrombolytic therapy, endovascular treatment and embolectomy. Readers may explore cutting edge research, recommendations from major societies, contemporary guidelines, areas of controversy and directions for ongoing and future research.
The book features comprehensive information ranging from molecular mechanisms of diseases to the clinical features, diagnosis, and therapeutic regimens for treating a variety of clinical conditions. It has a broad appeal to scientists and research students as well as busy clinicians engaged in patient care, who will all find something important and useful amongst these carefully selected chapters.
The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.
This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression.
This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis.
Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide.
The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided.
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.