Statistics and evidence-based medicine are assessed in most postgraduate and undergraduate medical examinations and degrees in health sciences. All clinicians have to acquire skills in this area. This book aims to provide a brief overview of basic medical statistics and the numerical aspects of evidence-based medicine, to give realistic worked examples to illustrate the interpretation of studies relevant to clinical practice, and to allow examination practice. It aims to cover all major topics covered in the undergraduate and postgraduate examinations.Each chapter begins with an overview and summary of the main points, followed by worked examples and exercises with full answers. It will be ideal for all postgraduate medical examination candidates. Other clinicians and undergraduate students in medicine and health sciences will also find it useful.

Medicine deals with treatments that work often but not always, so treatment success must be based on probability. Statistical methods lift medical research from the anecdotal to measured levels of probability. This book presents the common statistical methods used in 90% of medical research, along with the underlying basics, in two parts: a textbook section for use by students in health care training programs, e.g., medical schools or residency training, and a reference section for use by practicing clinicians in reading medical literature and performing their own research. The book does not require a significant level of mathematical knowledge and couches the methods in multiple examples drawn from clinical medicine, giving it applicable context. Easy-to-follow format incorporates medical examples, step-by-step methods, and check yourself exercises Two-part design features course material and a professional reference section Chapter summaries provide a review of formulas, method algorithms, and check lists Companion site links to statistical databases that can be downloaded and used to perform the exercises from the book and practice statistical methods New in this Edition: New chapters on: multifactor tests on means of continuous data, equivalence testing, and advanced methods New topics include: trial randomization, treatment ethics in medical research, imputation of missing data, and making evidence-based medical decisions Updated database coverage and additional exercises Expanded coverage of numbers needed to treat and to benefit, and regression analysis including stepwise regression and Cox regression Thorough discussion on required sample size

A comprehensive, yet easy to understand textbook that covers all of the main areas that are routinely tested in exams such as medical finals, the MRCP, and the FRCEM examinations. The text clearly explains the fundamental statistical principles used in the medical literature by concentrating on the essentials needed. Each chapter concludes with a self-assessed quiz that allows candidates to test themselves and reinforce key knowledge.

Learn to evaluate and apply statistics in medicine, medical research, and all health-related fields A Doody's Core Title for 2023! Basic & Clinical Biostatistics provides medical students, researchers, and practitioners with the knowledge needed to develop sound judgment about data applicable to clinical care. This fifth edition has been updated throughout to deliver a comprehensive, timely introduction to biostatistics and epidemiology as applied to medicine, clinical practice, and research. Particular emphasis is on study design and interpretation of results of research. The book features “Presenting Problems” drawn from studies published in the medical literature, end-of-chapter exercises, and a reorganization of content to reflect the way investigators ask research questions. To facilitate learning, each chapter contain a set of key concepts underscoring the important ideas discussed. Features: Key components include a chapter on survey research and expanded discussion of logistic regression, the Cox model, and other multivariate statistical methods Extensive examples illustrate statistical methods and design issues Updated examples using R, an open source statistical software package Expanded coverage of data visualization, including content on visual perception and discussion of tools such as Tableau, Qlik and MS Power BI Sampling and power calculations imbedded with discussion of the statistical model Updated content, examples, and data sets throughout

Now in its Fourth Edition, An Introduction to Medical Statistics continues to be a 'must-have' textbook for anyone who needs a clear logical guide to the subject. Written in an easy-to-understand style and packed with real life examples, the text clearly explains the statistical principles used in the medical literature. Taking readers through the common statistical methods seen in published research and guidelines, the text focuses on how to interpret and analyse statistics for clinical practice. Using extracts from real studies, the author illustrates how data can be employed correctly and incorrectly in medical research helping readers to evaluate the statistics they encounter and appropriately implement findings in clinical practice. End of chapter exercises, case studies and multiple choice questions help readers to apply their learning and develop their own interpretative skills. This thoroughly revised edition includes new chapters on meta-analysis, missing data, and survival analysis.

This is a basic book on evidence-based medicine (EBM). It starts with an introduction to the topic. It outlines the relationship between EBM and research and quality of care. Then It goes on to cover the most commonly used modules of EBM, i.e. therapy, diagnosis, prognosis and meta-analysis. Each module starts with an introduction to fundamental concepts, and description of the related research process, and then follows the critical appraisal of related type of research artcle. At the end, it covers the different systems of grading of level of evidence and strength of recommendations. The book also has three examples of critical appraisal on diagnosis, therapy, and meta-analysis.​

Explains the mathematics involved in understanding and choosing an array of diagnostic and prognostic tests, in order to improve treatment.