This textbook on statistics is written for students in medicine, epidemiology, and public health. It builds on the important role evidence-based medicine now plays in the clinical practice of physicians, physician assistants and allied health practitioners. By bringing research design and statistics to the fore, this book can integrate these skills into the curricula of professional programs. Students, particularly practitioners-in-training, will learn statistical skills that are required of today’s clinicians. Practice problems at the end of each chapter and downloadable data sets provided by the authors ensure readers get practical experience that they can then apply to their own work.

This concise, easy to understand and learner-friendly book invitesthe readers to actively particpate in the understanding of medicalstatistical concepts that are frequently used in health careresearch and evidence-based practice worldwide. Knowing that the best way to learn statistical concepts is touse them, the authors employ real examples and articles from healthscience literature, complete with the complexities that real lifepresents, in an approach that will help bring researchers andclinicians one step closer towards being statistical savvy andbetter able to critically read research literature and interpretthe results. A practical hands-on workbook for individual or groupexercises Teaches how to understand statistical methods when readingjournals, and how to use them in clinical research Emphasizes the use of statistics in evidence-basedresearch Relevant for anyone needing to use statistics, this workbook isan ideal resource for all health care professionals and students,especially those learning and practising evidence-basedmedicine.

A new edition of the classic guide to the use of statistics in medicine, featuring examples from articles in the New England Journal of Medicine Medical Uses of Statistics has served as one of the most influential works on the subject for physicians, physicians-in-training, and a myriad of healthcare experts who need a clear idea of the proper application of statistical techniques in clinical studies as well as the implications of their interpretation for clinical practice. This Third Edition maintains the focus on the critical ideas, rather than the mechanics, to give practitioners and students the resources they need to understand the statistical methods they encounter in modern medical literature. Bringing together contributions from more than two dozen distinguished statisticians and medical doctors, this volume stresses the underlying concepts in areas such as randomized trials, survival analysis, genetics, linear regression, meta-analysis, and risk analysis. The Third Edition includes: Numerous examples based on studies taken directly from the pages of the New England Journal of Medicine Two added chapters on statistics in genetics Two new chapters on the application of statistical methods to studies in epidemiology New chapters on analyses of randomized trials, linear regression, categorical data analysis, meta-analysis, subgroup analyses, and risk analysis Updated chapters on statistical thinking, crossover designs, p-values, survival analysis, and reporting research results A focus on helping readers to critically interpret published results of clinical research Medical Uses of Statistics, Third Edition is a valuable resource for researchers and physicians working in any health-related field. It is also an excellent supplemental book for courses on medicine, biostatistics, and clinical research at the upper-undergraduate and graduate levels. You can also visit the New England Journal of Medicine website for related information.

These days a basic knowledge of statistics is essential for good clinical practice, which presents a daunting challenge to health professionals who are not mathematically inclined. This book is aimed at clinicians and students who view statistics as a necessary evil. It covers the summary and presentation of data as might be required for a clinical meeting, audit or the planning of services, and explains how to interpret the p-values and confidence intervals that are reported in medical and scientific journals.

Converting Data into Evidence: A Statistics Primer for the Medical Practitioner provides a thorough introduction to the key statistical techniques that medical practitioners encounter throughout their professional careers. These techniques play an important part in evidence-based medicine or EBM. Adherence to EBM requires medical practitioners to keep abreast of the results of medical research as reported in their general and specialty journals. At the heart of this research is the science of statistics. It is through statistical techniques that researchers are able to discern the patterns in the data that tell a clinical story worth reporting. The authors begin by discussing samples and populations, issues involved in causality and causal inference, and ways of describing data. They then proceed through the major inferential techniques of hypothesis testing and estimation, providing examples of univariate and bivariate tests. The coverage then moves to statistical modeling, including linear and logistic regression and survival analysis. In a final chapter, a user-friendly introduction to some newer, cutting-edge, regression techniques will be included, such as fixed-effects regression and growth-curve modeling. A unique feature of the work is the extensive presentation of statistical applications from recent medical literature. Over 30 different articles are explicated herein, taken from such journals. With the aid of this primer, the medical researcher will also find it easier to communicate with the statisticians on his or her research team. The book includes a glossary of statistical terms for easy access. This is an important reference work for the shelves of physicians, nurses, nurse practitioners, physician’s assistants, medical students, and residents.

This comprehensive, graduate-level text for advanced practice nurses and other health care professionals provides state-of-the-art tools that facilitate the reading and interpretation of clinical research articles that use increasingly complex statistical techniques. It addresses clinically relevant topics in biostatistics beyond the usual introduction to linear models, such as survival analysis and evaluation of screening tests. The text emphasizes the importance of understanding the underlying logic of statistical inference and statistical models to support correct interpretation and effective translation into practice. It promotes appropriate statistical method selection for conducting translational research. With a focus on disseminating information in easily understandable language, the text addresses basic statistical reasoning and four different classes of statistical models. The appendix provides refreshers on the algebraic underpinnings of statistics. More complex algebraic derivations are highlighted in boxes throughout the text chapters. The text explains how to work with the exponential expressions and logarithms necessary for the interpretation of logistic and hazard regression models and features clear explanations of more sophisticated statistical models, inference, and analyses. Chapters include examples from current research and multiple exercises designed to reinforce learning. Key Features: End-of-chapter exercises include both problems of interpretation and numerical problems that can be solved via hand calculations. For instructors and students interested in practical data analysis, data sets and practice problems are available from Springer Publishing Companyís website with instructions in SPSS, STATA, and SAS formats. At the end of each chapter is a ìLiterature Applicationî text box with interpretation questions about a recent research article that highlights the statistical model discussed in the chapter. Throughout the book, text boxes highlight the most important algebraic formulas useful in interpreting statistical methods. A chapter on data management practices and ethical issues of privacy maintenance is included. Nine appendices provide tables of major probability distributions, for example, normal, t- and F-distributions, and algebraic derivations of some of the most important results in statistics.

Thanks to the omnipresent computer, current statistics can include data files of many thousands of values, and can perform any exploratory analysis in less than seconds. This development, however fascinating, generally does not lead to simple results. We should not forget that clinical studies are, mostly, for confirming prior hypotheses based on sound arguments, and the simplest tests provide the best power and are adequate for such studies. In the past few years the authors of this 5th edition, as teachers and research supervisors in academic and top-clinical facilities, have been able to closely observe the latest developments in the field of clinical data analysis, and they have been able to assess their performance. In this 5th edition the 47 chapters of the previous edition have been maintained and upgraded according to the current state of the art, and 20 novel chapters have been added after strict selection of the most valuable and promising novel methods. The novel methods are explained using practical examples and step-by-step analyses readily accessible for non-mathematicians. All of the novel chapters have been internationally published by the authors in peer-reviewed journal, including the American Journal of Therapeutics, the European Journal of Clinical Investigation, The International journal of Clinical Pharmacology and therapeutics, and other journals, and permission is granted by all of them to use this material in the current book. We should add that the authors are well-qualified in their fields of knowledge. Professor Zwinderman is president-elect of the International Society of Biostatistics, and Professor Cleophas is past-president of the American College of Angiology. From their expertise they should be able to make adequate selections of modern methods for clinical data analysis for the benefit of physicians, students, and investigators. The authors, although from a different discipline, one clinician and one statistician, have been working and publishing together for over 10 years, and their research of statistical methodology can be characterized as a continued effort to demonstrate that statistics is not mathematics but rather a discipline at the interface of biology and mathematics. They firmly believe that any reader can benefit from this clinical approach to statistical data analysis.