Statistical science plays an increasingly important role in medical research. Over the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers and, with the appropriate software now easily available, these techniques can be used almost routinely to great effect. These innovative methods include survival analysis, generalized additive models and Bayesian methods. Modern Medical Statistics covers these essential new techniques at an accessible technical level, its main focus being not on the theory but on the effective practical application of these methods in medical research. Modern Medical Statistics is an indispensable practical guide for medical researchers and medical statisticians as well as an ideal text for advanced courses in medical statistics and public health.

Holistic approach to understanding medical statistics This hands-on guide is much more than a basic medical statistics introduction. It equips you with the statistical tools required for evidence-based clinical research. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. Showing you how to: analyse data with the help of data set examples (Click here to download datasets) select the correct statistics and report results for publication or presentation understand and critically appraise results reported in the literature Each statistical test is linked to the research question and the type of study design used. There are also checklists for critically appraising the literature and web links to useful internet sites. Clear and concise explanations, combined with plenty of examples and tabulated explanations are based on the authors’ popular medical statistics courses. Critical appraisal guidelines at the end of each chapter help the reader evaluate the statistical data in their particular contexts.

The majority of medical research involves quantitative methods and so it is essential to be able to understand and interpret statistics. This book shows readers how to develop the skills required to critically appraise research evidence effectively, and how to conduct research and communicate their findings.

Blackwell Publishing is delighted to announce that this book hasbeen Highly Commended in the 2004 BMA Medical Book Competition.Here is the judges' summary of this book: "This is a technical book on a technical subject but presentedin a delightful way. There are many books on statistics for doctorsbut there are few that are excellent and this is certainly one ofthem. Statistics is not an easy subject to teach or write about.The authors have succeeded in producing a book that is as good asit can get. For the keen student who does not want a book formathematicians, this is an excellent first book on medicalstatistics." Essential Medical Statistics is a classic amongst medicalstatisticians. An introductory textbook, it presents statisticswith a clarity and logic that demystifies the subject, whileproviding a comprehensive coverage of advanced as well as basicmethods. The second edition of Essential Medical Statistics hasbeen comprehensively revised and updated to include modernstatistical methods and modern approaches to statistical analysis,while retaining the approachable and non-mathematical style of thefirst edition. The book now includes full coverage of the mostcommonly used regression models, multiple linear regression,logistic regression, Poisson regression and Cox regression, as wellas a chapter on general issues in regression modelling. Inaddition, new chapters introduce more advanced topics such asmeta-analysis, likelihood, bootstrapping and robust standarderrors, and analysis of clustered data. Aimed at students of medical statistics, medical researchers,public health practitioners and practising clinicians usingstatistics in their daily work, the book is designed as both ateaching and a reference text. The format of the book is clear withhighlighted formulae and worked examples, so that all concepts arepresented in a simple, practical and easy-to-understand way. Thesecond edition enhances the emphasis on choice of appropriatemethods with new chapters on strategies for analysis and measuresof association and impact. Essential Medical Statistics is supported by a web siteat www.blackwellpublishing.com/essentialmedstats. Thisuseful online resource provides statistical datasets to download,as well as sample chapters and future updates.

The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies

Explains the purpose of statistical methods in medical studies & analyzes the statistical techniques used by clinical investigators, with special emphasis on studies published in The New England Journal of Medicine. Clarifies fundamental concepts of statistical design & analysis & facilitates the understanding of research results.

Argues that the pace of medical discoveries has slowed in the last twenty-five years due to excessive emphasis on the social and political aspects of health care, and to controversies caused by ethical issues.

Drawing on the work of the Roundtable on Evidence-Based Medicine, the 2007 IOM Annual Meeting assessed some of the rapidly occurring changes in health care related to new diagnostic and treatment tools, emerging genetic insights, the developments in information technology, and healthcare costs, and discussed the need for a stronger focus on evidence to ensure that the promise of scientific discovery and technological innovation is efficiently captured to provide the right care for the right patient at the right time. As new discoveries continue to expand the universe of medical interventions, treatments, and methods of care, the need for a more systematic approach to evidence development and application becomes increasingly critical. Without better information about the effectiveness of different treatment options, the resulting uncertainty can lead to the delivery of services that may be unnecessary, unproven, or even harmful. Improving the evidence-base for medicine holds great potential to increase the quality and efficiency of medical care. The Annual Meeting, held on October 8, 2007, brought together many of the nation's leading authorities on various aspects of the issues - both challenges and opportunities - to present their perspectives and engage in discussion with the IOM membership.

Praise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.