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Medical Statistics at a Glance Description

Medical Statistics at a Glance is a concise and accessible introduction and revision aid for this complex subject. The self-contained chapters explain the underlying concepts of medical statistics and provide a guide to the most commonly used statistical procedures.

This new edition of Medical Statistics at a Glance:

• Presents key facts accompanied by clear and informative tables and diagrams
• Focuses on illustrative examples which show statistics in action, with an emphasis on the interpretation of computer data analysis rather than complex hand calculations
• Includes extensive cross-referencing, a comprehensive glossary of terms and flow-charts to make it easier to choose appropriate tests
• Now provides the learning objectives for each chapter
• Includes a new chapter on Developing Prognostic Scores
• Includes new or expanded material on study management, multi-centre studies, sequential trials, bias and different methods to remove confounding in observational studies, multiple comparisons, ROC curves and checking assumptions in a logistic regression analysis
• The companion website at www.medstatsaag.com contains supplementary material including an extensive reference list and multiple choice questions (MCQs) with interactive answers for self-assessment.

Medical Statistics at a Glance will appeal to all medical students, junior doctors and researchers in biomedical and pharmaceutical disciplines.

Reviews of the previous editions

"The more familiar I have become with this book, the more I appreciate the clear presentation and unthreatening prose. It is now a valuable companion to my formal statistics course."
–International Journal of Epidemiology

"I heartily recommend it, especially to first years, but it's equally appropriate for an intercalated BSc or Postgraduate research. If statistics give you headaches - buy it. If statistics are all you think about - buy it."
–GKT Gazette

"...I unreservedly recommend this book to all medical students, especially those that dislike reading reams of text. This is one book that will not sit on your shelf collecting dust once you have graduated and will also function as a reference book."
–4th Year Medical Student, Barts and the London Chronicle, Spring 2003

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Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings.

An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real-life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease.

To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included.

It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas.

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This thought-provoking book discusses the use of statistics in randomized clinical trials. Its aim is two-fold: firstly, it presents a clear account of the design and analysis of experiments in this setting which stresses the foundational issues involved. Secondly, the book seeks to develop the specific tools of analysis which can be derived from Neyman's model of restricted tests. The book is based on the author's many years of experience of clinical trials. Throughout, examples are used from a variety of types of study. As a result, all statisticians and research scientists who work on clinical trials will find this presentation clear and accessible, and very relevant to their own research interests.

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Public health strives to improve the health of human populations, and prevent disease, disability, and death. Statistics--the science of finding underlying patterns by analyzing variability and errors in collected data--is essential to the understanding of disease patterns in human populations. Other quantitative methods, such as economics, decision theory, and mathematics, now constitute integral parts of the scientific basis for priority-setting and evaluation in public health.
This book provides a broad conceptual treatment of the statistical issues underlying core public health functions: outbreak investigations, policy development, economic and program evaluation, managed care, and program operations. The theoretical analysis is illustrated with examples from public health practice. For readers interested in a more detailed treatment, there are extensive references to specialized publications.
The authors present a series of quantitative approaches that significantly help public health professionals solve the problems they confront in their day-to-day work. Unlike traditional how-to books in statistics, this volume starts with an overview of the range of problems encountered in public health practice, and then presents methods for facilitating decision making. Statistics in Public Health: Quantitative Approaches to Public Health Problems will serve as a comprehensive desk reference for public health practitioners and as a teaching text for students of public health.

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Selection Bias and Covariate Imbalances in Randomized Clinical Trials (Statistics in Practice) Description

Selection bias can, and does, occur, even in randomized clinical trials. Steps need to be taken in order to ensure that this does not compromise the integrity of clinical trials; hence “Selection Bias and Covariate Imbalances in Randomized Clinical Trials" offers a comprehensive treatment of the subject and the methodology involved.  This book:

• Provides an overview of the hierarchy of study designs, and justifies the position of randomised trials at the top of this hierarchy.
• Discusses the strengths and defects of randomisation, and provides real evidence to justify concern regarding its defects.
• Outlays the damaging consequences that selection bias causes when it does occur.
• Considers courses of action that can be taken to manage/ contain the problem.
• Presents methods that can be used to detect selection bias in randomised trials, and methods to correct for selection bias.
• Concludes by providing a comprehensive plan for managing baseline imbalances and selection bias in randomised trials, and proposing open problems for future research.

 Illustrated with case studies, this book introduces groundbreaking ideas and research that will be invaluable to researchers and practitioners who design and analyse clinical trials. It will also be of interest to graduate students within the field of biostatistics. 

Best Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science)

Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science) by Chapman and Hall/CRC




Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research.

Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background.

The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.Read more Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science)

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This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Introduction to Meta-Analysis:

• Outlines the role of meta-analysis in the research process
• Shows how to compute effects sizes and treatment effects
• Explains the fixed-effect and random-effects models for synthesizing data
• Demonstrates how to assess and interpret variation in effect size across studies
• Clarifies concepts using text and figures, followed by formulas and examples
• Explains how to avoid common mistakes in meta-analysis
• Discusses controversies in meta-analysis
• Features a web site with additional material and exercises

A superb combination of lucid prose and informative graphics, written by four of the world’s leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and Rothstein provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students, who used pre-publication versions of some of the chapters, raved about the clarity of the explanations and examples. David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics.

The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice. Jesse A. Berlin, ScD 

Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University

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Clinical Statistics: Introducing Clinical Trials, Survival Analysis, and Longitudinal Data Analysis provides the mathematic background necessary for students preparing for a career as a statistician in the biomedical field. The manual explains the steps a clinical statistician must take in clinical trials from protocol writing to subject randomization, to data monitoring, and on to writing a final report to the FDA. All of the necessary fundamentals of statistical analysis: survival and longitudinal data analysis are included. SAS procedures are explained with simple examples and the mathematics behind these SAS procedures are covered in detail with the statistical software program SAS which is implemented throughout the text. Complete codes are given for every example found in the text. The exercises featured throughout the guide are both theoretical and applied making it appropriate for those moving on to different clinical settings. Students will find Clinical Statistics to be a handy lab reference for coursework and in their future careers.

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This work explains the purpose of statistical methods in medical studies and analyzes the statistical techniques used by clinical investigators, with special emphasis on studies published in "The New England Journal of Medicine". It clarifies fundamental concepts of statistical design and analysis, and facilitates the understanding of research results.

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This pioneering introductory statistics text was the first written specifically to make statistics accessible for health science students. Assuming no prerequisites other than high school algebra, the authors provide numerous examples from health settings, a wealth of helpful learning aids, as well as hundreds of exercises that help students succeed in the course.

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This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by worked examples. The examples are drawn from all areas of biology, from biochemistry to ecology and from cell to animal biology. The book provides everything required in an introductory statistics course for biology undergraduates, and it is also useful for more specialized undergraduate courses in ecology, botany, and zoology.

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Methods for Meta-Analysis in Medical Research (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section) by Wiley




With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the methods by numerous examples
* Describes the use of Bayesian methods in meta-analysis
* Includes discussion of appropriate software for each analysis
* Includes numerous references to more advanced treatment of specialist topics
* Refers to software code used in the examples available on the authors' Web site
Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.Read more Methods for Meta-Analysis in Medical Research (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

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An Introduction to Medical Statistics (Oxford Medical Publications) by Oxford University Press, USA




Now in its Third Edition, An Introduction to Medical Statistics continues to be and invaluable textbook for medical students, doctors, medical researchers, nurses, members of professionals allied to medicine as well as those concerned with medical data. The material covered includes all the statistical work that would be required for a course in medicine and for the examinations of most of the Royal Colleges. It includes the design of clinical trials and epidemiological studies, data collection, summarizing and presenting data, probability, standard error, confidence intervals and significance tests, techniques of data analysis including multifactorial methods and the choice of statistical method, problems of medical measurement and diagnosis, vital statistics, and calculation of sample size. The new edition describes the design and analysis of medical research studies in a clear and user friendly manner. The third edition includes new topics such as consent in clinical trials, design and analysis of cluster-randomized trials, ecological studies, conditional probability, repeated testing, random effects models, intraclass correlation and conditional odds ratios. Material which is encountered only at the postgraduate level has been indicated clearly in the text to facilitate ease or use. The book is firmly grounded in medical data, particularly in medical research, and includes real illustrative examples. There are 100 multiple choice questions and 17 long questions involving calculations to which fully explained solutions are provided. A new companion volume, Statisitical questions in evidence based medicine (Bland & Peacock, 2000) refers directly to this new edition. This new book of questions and answers includes no calculations and is complementary to the exercises given here.Read more An Introduction to Medical Statistics (Oxford Medical Publications)

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Clinical Trials in Oncology, Third Edition (Chapman & Hall/CRC Interdisciplinary Statistics) by CRC Press

The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.

Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

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Praise for the First Edition

". . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists."
—International Statistical Institute

A new edition of the definitive guide to classical and modern methods of biostatistics

Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data.

Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include:

• Establishing equivalence and non-inferiority
• Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index
• Multinomial logistic for polychotomous data and proportional odds models for ordinal data
• Negative binomial models for count data as an alternative to the Poisson model
• GEE models for the analysis of longitudinal repeated measures and multivariate observations

Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines.

Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.

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As many medical and healthcare researchers have a love-hate relationship with statistics, this practical reference book may make all the difference. It takes examples, mainly from the authors' own research, to explain how to make sense of statistics, turn statistical computer results into coherent information, and help decide which pieces of information to report and how to present them. Presenting Medical Statistics includes a wide range of statistical analyses, and all the statistical methods are illustrated using real data. Labelled figures show the Stata and SPSS commands needed to obtain the analyses, with indications of which information should be extracted from the output for reporting. The relevant results are then presented as for a report or journal article, to illustrate the principles of good presentation. The reader is taken through the various stages of the research process, from the initial research proposal, ethical approval and data analysis, to reporting on and publishing the findings. There are even extensive references for those who wish to find out more about the statistical methods. This is a must for anyone working with statistics in the medical profession.