Author: Martin Bland
Publisher: Oxford University Press (UK)
Release Date: 2015-07
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 principlesused 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 medicalresearch 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.
Author: Martin Bland
Publisher: OUP Oxford
Release Date: 2015-07-23
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.
Author: Janet Peacock
Publisher: Oxford University Press
Release Date: 2011
Genre: Language Arts & Disciplines
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.
Author: Douglas G. Altman
Publisher: CRC Press
Release Date: 1990-11-22
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.
Author: Ramakrishna HK
Release Date: 2016-12-13
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 textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.
Author: Janet L. Peacock
Publisher: Oxford University Press
Release Date: 2007
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.
Author: Michael J. Campbell
Publisher: John Wiley & Sons
Release Date: 2010-10-26
Provides students and practitioners with a clear, concise introduction to the statistics they will come across in their regular reading of clinical papers. Written by three experts with wide teaching and consulting experience, Medical Statistics: A Textbook for the Health Sciences, Fourth Edition: Assumes no prior knowledge of statistics Covers all essential statistical methods Completely revised, updated and expanded Includes numerous examples and exercises on the interpretation of the statistics in papers published in medical journals From the reviews of the previous edition: "The book has several excellent features: it is written by statisticians, is.... well presented, is well referenced.... and is short." THE LANCET "Many statisticians are concerned at the generally poor standard of statistics in papers published in medical journals. Perhaps this could be remedied if more research workers would spare a few hours to read through Campbell and Machin's book." BRITISH MEDICAL JOURNAL "... a simple, interesting and insightful introduction to medical statistics... highly recommended." STATISTICAL METHODS IN MEDICAL RESEARCH "Campbell and Machin found the golden mean... this book can be recommended for all students and all medical researchers." ISCB NEWSLETTER
Author: Aviva Petrie
Publisher: John Wiley & Sons
Release Date: 2012-12-13
This comprehensive workbook contains a variety of self-assessment methods that allow readers to test their statistical knowledge, put it into practice, and apply it in a medical context, while also providing guidance when critically appraising published literature. It is designed to support the best-selling third edition of Medical Statistics at a Glance, to which it is fully cross-referenced, but may be used independently of it. Ideal for medical students, junior doctors, researchers and anyone working in the biomedical and pharmaceutical disciplines who wants to feel more confident in basic medical statistics, the title includes: • Over 80 MCQs, each testing knowledge of a single statistical concept or aspect of study interpretation • 29 structured questions to explore in greater depth several statistical techniques or principles, including the choice of appropriate statistical analyses and the interpretation of study findings • Templates for the appraisal of clinical trials and observational studies, plus full appraisals of two published papers to demonstrate the use of these templates in practice • Detailed step-by-step analyses of two substantial data sets (also available at www.medstatsaag.com) to demonstrate the application of statistical procedures to real-life research Medical Statistics at a Glance Workbook is the ideal resource to test statistical knowledge and improve analytical and interpretational skills. Additional resources are available at www.medstatsaag.com, including: • Excel datasets to accompany the data analysis section • Downloadable PDFs of two templates for critical appraisal • Links to online further reading • Supplementary MCQs
Author: Betty R. Kirkwood
Publisher: John Wiley & Sons
Release Date: 2010-09-16
Blackwell Publishing is delighted to announce that this book has been 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 presented in a delightful way. There are many books on statistics for doctors but there are few that are excellent and this is certainly one of them. Statistics is not an easy subject to teach or write about. The authors have succeeded in producing a book that is as good as it can get. For the keen student who does not want a book for mathematicians, this is an excellent first book on medical statistics." Essential Medical Statistics is a classic amongst medical statisticians. An introductory textbook, it presents statistics with a clarity and logic that demystifies the subject, while providing a comprehensive coverage of advanced as well as basic methods. The second edition of Essential Medical Statistics has been comprehensively revised and updated to include modern statistical methods and modern approaches to statistical analysis, while retaining the approachable and non-mathematical style of the first edition. The book now includes full coverage of the most commonly used regression models, multiple linear regression, logistic regression, Poisson regression and Cox regression, as well as a chapter on general issues in regression modelling. In addition, new chapters introduce more advanced topics such as meta-analysis, likelihood, bootstrapping and robust standard errors, and analysis of clustered data. Aimed at students of medical statistics, medical researchers, public health practitioners and practising clinicians using statistics in their daily work, the book is designed as both a teaching and a reference text. The format of the book is clear with highlighted formulae and worked examples, so that all concepts are presented in a simple, practical and easy-to-understand way. The second edition enhances the emphasis on choice of appropriate methods with new chapters on strategies for analysis and measures of association and impact. Essential Medical Statistics is supported by a web site at www.blackwellpublishing.com/essentialmedstats. This useful online resource provides statistical datasets to download, as well as sample chapters and future updates.
Author: Ying Lu
Publisher: World Scientific
Release Date: 2015-06-29
The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch. The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field. Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research. Contents:Statistics in Medicine and Epidemiology:History of Statistical Thinking in Medicine (Tar Timothy Chen)Describing Data, Modeling Variation, and Statistical Practice (Hongyan Du and Ming T Tan)Covariate-Specific and Covariate-Adjusted Predictive Values of Prognostic Biomarkers with Survival Outcome (Yunbei Ma, Xiao-Hua Zhou and Kwun Chuen (Gary) Chan)Statistical Methods for Personalized Medicine (Lu Tian and Xiaoguang Zhao)Statistics Used in Quality Control, Quality Assurance, and Quality Improvement in Radiological Studies (Ying Lu and Shoujun Zhao)Applications of Statistical Methods in Medical Imaging (Jesse S Jin)Cost-Effectiveness Analysis and Evidence-Based Medicine (Jianli Li)Quality of Life: Issues Concerning Assessment and Analysis (Jiqian Fang and Yuantao Hao)Meta-Analysis (Xuyu Zhou, Jiqian Fang, Chuanhua Yu, Zongli Xu, Lu Tian, and Ying Lu)Statistical Models and Methods in Infectious Diseases (Hulin Wu and Shoujun Zhao)Special Models for Sampling Survey (Sujuan Gao)The Use of Capture–Recapture Methodology in Epidemiological Surveillance and Ecological Surveys (Anne Chao, T C Hsieh and Hsin-Chou Yang)Statistical Methods in the Effective Evaluation of Mass Screening for Diseases (Qing Liu)Statistics in Clinical Trials:Statistics in Biopharmaceutical Research and Development (Shein-Chung Chow and Annpey Pong)Statistics in Pharmacology and Pre-Clinical Studies (Tze Leung Lai, Mei-Chiung Shin and Guangrui Zhu)Statistics in Toxicology (James J Chen)Dose-Response Modeling and Benchmark Doses in Health Risk Assessment (Yiliang Zhu)Some Fundamental Statistical Issues and Methodologies in Confirmatory Trials (George Y H Chi, Haiyan Xu and Qing Liu)Surrogates for Qualitative Evaluation of Treatment Effects (Zhi Geng)Adaptive Trial Design in Clinical Research (Annpey Pong and Shein-Chung Chow)Statistics in the Research of Traditional Chinese Medicine (Danhui Yi and Yang Li)Statistical Genetics:Sparse Segment Identifications with Applications to DNA Copy Number Variation Analysis (X Jessie Jeng, T Tony Cai and Hongzhe Li)Statistical Methods for Design and Analysis of Linkage Studies (Qizhai Li, Hong Qin, Zhaohai Li, and Gang Zheng)Transcriptome Analysis Using Next-Generation Sequencing (Jingyi Jessica Li, Haiyan Huang, Minping Qian and Xuegong Zhang)Genetic Structure of Human Population (Hua Tang and Kun Tang)Data Integration Methods in Genome Wide Association Studies (Ning Sun and Hongyu Zhao)Causal Inference (Zhi Geng)General Methods:Survival Analysis (D Y Lin)Nonparametric Regression Models for the Analysis of Longitudinal Data (Colin O Wu, Xin Tian, Kai F Yu, and Mi-Xia Wu)Local Modeling: Density Estimation and Nonparametric Regression (Jianqing Fan and Runze Li)Statistical Methods for Dependent Data (Feng Chen)Bayesian Methods (Ming-Hui Chen and Keying Ye)Valid Prior-Free Probabilistic Inference and Its Applications in Medical Statistics (Duncan Ermini Leaf, Hyokun Yun, and Chuanhai Liu)Stochastic Processes and Their Applications in Medical Science (Caixia Li and Jiqian Fang)Interpolation of Missing Values and Adjustment of Moving Holiday Effect in Time Series (Zhang Jin-Xin, Zhang Xi, Xue Yun-Lian, Li Ji-Bin and Huang Bo)Tree-based Methods (Heping Zhang)Introduction to Artificial Neural Networks (Xia Jielai, Jiang Hongwei, and Tang Qiyi) Readership: Biostatisticians, applied statisticians, medical researchers and clinicians, biopharmaceutical researchers, public health epidemiologists, biometricians and applied mathematicians. Key Features: The book covers very broad topics in medical statistics The book covers both most recent developments as well as classical work of the selected areas The book chapter is written by the experts in the field and illustrated with real life examplesKeywords:Medicine;Statistics;Epidemiology;Genomics;Clinical Trials;Bioinformatics;Machine Learning;Statistical Theory;Public HealthReviews: Review of the First Edition: “Overall the book covers a wide variety of applications. Each method is presented in sufficient depth to allow the reader to understand when the method(s) can be used … this book would be a useful resource for any practitioner in medical research.” Statistical Methods in Medical Research
Author: Piers Page
Publisher: Oxford University Press
Release Date: 2012
An introductory guide to clinical research, written specifically for junior doctors by a team of highly experienced authors. This practical book covers all areas that a junior doctor will need to consider, including funding, study design, ethics, data analysis, disseminating findings, and furthering one's research career.
Author: Bryan Kestenbaum
Publisher: Springer Science & Business Media
Release Date: 2009-08-28
Concise, fast-paced, intensive introduction to clinical research design for students and clinical research professionals Readers will gain sufficient knowledge to pass the United States Medical Licensing Examination part I section in Epidemiology
Author: John C. Bailar
Publisher: John Wiley & Sons
Release Date: 2012-01-10
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.