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Statistical Thinking in Epidemiology by Yu-Kang Tu,Mark Gilthorpe Pdf
While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems
Author : Graham R. Law and Shane W. Pascoe Publisher : CABI Page : 231 pages File Size : 44,7 Mb Release : 2012 Category : Electronic ISBN : 1780641338
Statistical Epidemiology by Graham R. Law and Shane W. Pascoe Pdf
Statistics are a vital skill for epidemiologists and form an essential part of clinical medicine and public health. This textbook introduces students to statistical epidemiology methods in a carefully structured and accessible format. With clearly defined learning outcomes, the suggested chapter orders can be tailored to the needs of students at both undergraduate and graduate level from a range of academic backgrounds. The book covers study design, measuring disease, bias, error, analysis and modelling and is illustrated with figures, focus boxes, study questions and examples applicable to ev.
Statistical Thinking in Clinical Trials by Michael A. Proschan Pdf
Statistical Thinking in Clinical Trials combines a relatively small number of key statistical principles and several instructive clinical trials to gently guide the reader through the statistical thinking needed in clinical trials. Randomization is the cornerstone of clinical trials and randomization-based inference is the cornerstone of this book. Read this book to learn the elegance and simplicity of re-randomization tests as the basis for statistical inference (the analyze as you randomize principle) and see how re-randomization tests can save a trial that required an unplanned, mid-course design change. Other principles enable the reader to quickly and confidently check calculations without relying on computer programs. The `EZ’ principle says that a single sample size formula can be applied to a multitude of statistical tests. The `O minus E except after V’ principle provides a simple estimator of the log odds ratio that is ideally suited for stratified analysis with a binary outcome. The same principle can be used to estimate the log hazard ratio and facilitate stratified analysis in a survival setting. Learn these and other simple techniques that will make you an invaluable clinical trial statistician.
"Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. New to this edition: Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. Chapter 21 contrasts testing for equivalence with testing for differences. Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. Chapters 24 and 25 review testing for normality and outliers. Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. New "Q and A" sections throughout the book review key concepts"--Provided by publisher.
Statistics for Epidemiology by Nicholas P. Jewell Pdf
Statistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes." Statistics for Epidemiology achieves just the right balance between the two approaches, building an intuitive understanding of the methods most important to practitioners and the skills to use them effectively. It develops the techniques for analyzing simple risk factors and disease data, with step-by-step extensions that include the use of binary regression. It covers the logistic regression model in detail and contrasts it with the Cox model for time-to-incidence data. The author uses a few simple case studies to guide readers from elementary analyses to more complex regression modeling. Following these examples through several chapters makes it easy to compare the interpretations that emerge from varying approaches. Written by one of the top biostatisticians in the field, Statistics for Epidemiology stands apart in its focus on interpretation and in the depth of understanding it provides. It lays the groundwork that all public health professionals, epidemiologists, and biostatisticians need to successfully design, conduct, and analyze epidemiological studies.
Basic Concepts in Statistics and Epidemiology by Theodore H. MacDonald,Denis Pereira Gray Pdf
This book contains a Foreword by Allyson Pollock, Professor and Head, Centre for International Public Health Policy, University of Edinburgh. Healthcare students, practitioners and researchers need a sound basis for making valid statistical inferences from health data. To make the best use of statistical software, it is necessary to understand how probabilistic inference works. This book explains that, along with the various ways statistical data can be described and presented. It is designed to develop insight rather than simply the mechanical skills found in other textbooks. This book is specifically designed to underpin the concepts of statistics and epidemiology. It is practical and easy to use and is ideal for people who can feel uncomfortable with mathematics. 'Excellent. A great primer for all students and research workers engaged in learning how to use statistical ideas in public health. It sets out the core concepts and explains them clearly, using worked examples as illustration. If followed carefully, the engaged reader should be able to use the standard statistical software packages intelligently and sensitively. It will stimulate the public health student, in whatever context, and new researchers, to approach the enterprise with enhanced confidence in interpreting and coherently explaining their findings.' - Allyson Pollock, in the Foreword.
Modern Methods for Epidemiology by Yu-Kang Tu,Darren C. Greenwood Pdf
Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensible tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology.
Advanced Medical Statistics by Ying Lu,Ji-Qian Fang Pdf
Ch. 1. History of statistical thinking in medicine / Tar Timothy Chen -- ch. 2. Evaluation of diagnostic test's accuracy in the presence of verification bias / Xiao-Hua Zhou -- ch. 3. Statistical methods for dependent data / Feng Chen -- ch. 4. Statistics used in quality control, quality assurance, and quality improvement in radiological studies / Ying Lu and Shoujun Zhao -- ch. 5. Cost-effectiveness analysis and evidence-based medicine / Jianli Li -- ch. 6. Quality of life: issues concerning assessment and analysis / Ji-Qian Fang and Yuantao Hao -- ch. 7. Meta-analysis / Xuyu Zhuo ... [et al.] -- ch. 8. Describing data, variability and over-dispersion in medical research / Ming Tan -- ch. 9. Time series analysis and its applications in medical sciences / Jinxi Zhang, Yingdong Zheng and Dejian Lai -- ch.10. Applications of statistical methods in medical imaging / Jesse S. Jin -- ch.11. Statistics in pharmacology and pre-clinical studies / Tze Leung Lai, Mei-Chiung Shin and Guangrui Zhu -- ch.12. Statistics in biopharmaceutical research / Shein-Chung Chow and Annpey Pong -- ch.13. Statistics in toxicology / James J. Chen -- ch. 14. Some statistical issues of relevence to confirmatory trials / George Y. H. Chi ... [et al.] -- ch. 15. Statistics in genetics / Zhaohai Li and Minyu Xie -- ch. 16. Dose-response modeling in health risk assessment / Yiliang Zhu -- ch. 17. Statistical models and methods in infectious diseases / Hulin Wu and Shoujun Zhao -- ch. 18. Special models for sampling survey / Sujuan Gao -- ch. 19. The use of capture-recapture methodology in epidemiological surveillance / Anne Chao, H-C. Yang and P. S. F. Yip -- ch. 20. Statistical methods in the effect evaluation of mass screening for diseases / Qing Liu -- ch. 21. Causal inference / Zhi Geng -- ch. 22. Survival analysis / Danyu Lin -- ch. 23. Regression models for the analysis of longitudinal data / Colin Wu and Kai F. Yu -- ch. 24. Local modeling: density estimation and nonparametric regression / Jianqing Fan and Runze Li -- ch. 25. Bayesian methods / Minghui Chen and Keying Ye -- ch. 26. Stochastic process and their applications in medical science / Caixia Li and Ji-Qian Fang -- ch. 27. Tree-based methods / Heping Zhang -- ch. 28. Maximum likelihood estimation from incomplete data via EM-type algorithms / Chuanhai Liu -- ch. 29. Introduction to artificial neural networks / Jielai Xia, Jiang Hongwei and Tang Qiyi
Author : John C. Bailar,David C. Hoaglin Publisher : John Wiley & Sons Page : 393 pages File Size : 43,9 Mb Release : 2012-01-10 Category : Medical ISBN : 9781118211182
Medical Uses of Statistics by John C. Bailar,David C. Hoaglin Pdf
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.
Statistical Analysis of Epidemiologic Data by Steve Selvin Pdf
Analytic procedures suitable for the study of human disease are scattered throughout the statistical and epidemiologic literature. Explanations of their properties are frequently presented in mathematical and theoretical language. This well-established text gives readers a clear understanding of the statistical methods that are widely used in epidemiologic research without depending on advanced mathematical or statistical theory. By applying these methods to actual data, Selvin reveals the strengths and weaknesses of each analytic approach. He combines techniques from the fields of statistics, biostatistics, demography and epidemiology to present a comprehensive overview that does not require computational details of the statistical techniques described. For the Third Edition, Selvin took out some old material (e.g. the section on rarely used cross-over designs) and added new material (e.g. sections on frequently used contingency table analysis). Throughout the text he enriched existing discussions with new elements, including the analysis of multi-level categorical data and simple, intuitive arguments that exponential survival times cause the hazard function to be constant. He added a dozen new applied examples to illustrate such topics as the pitfalls of proportional mortality data, the analysis of matched pair categorical data, and the age-adjustment of mortality rates based on statistical models. The most important new feature is a chapter on Poisson regression analysis. This essential statistical tool permits the multivariable analysis of rates, probabilities and counts.
Statistical Methods in Epidemiologic Research by Ray M. Merrill Pdf
"With the many advances in the control of infectious disease over the last 100 years, the role of epidemiology in public health has transformed significantly. Epidemiologic research now includes the study of acute and chronic diseases, as well as the events, behaviors, and conditions associated with health. From seasoned author Ray Merrill, this text explores how epidemiologic methods are conducted and interpreted. In four sections, Statistical Methods in Epidemiologic Research covers basic concepts in epidemiology and statistics, study designs, statistical techniques and applications, as well as special topics."--Publisher's website.
The Challenge of Developing Statistical Literacy, Reasoning and Thinking by Dani Ben-Zvi,Joan Garfield Pdf
Unique in that it collects, presents, and synthesizes cutting edge research on different aspects of statistical reasoning and applies this research to the teaching of statistics to students at all educational levels, this volume will prove of great value to mathematics and statistics education researchers, statistics educators, statisticians, cognitive psychologists, mathematics teachers, mathematics and statistics curriculum developers, and quantitative literacy experts in education and government.
Handbook of Epidemiology by Wolfgang Ahrens,Iris Pigeot Pdf
The Handbook of Epidemiology provides a comprehensive overview of the field and thus bridges the gap between standard textbooks of epidemiology and dispersed publications for specialists that have a narrowed focus on specific areas. It reviews the key issues and methodological approaches pertinent to the field for which the reader pursues an expatiated overview. It thus serves both as a first orientation for the interested reader and as a starting point for an in-depth study of a specific area, as well as a quick reference and recapitulatory overview for the expert. The book includes topics that are usually missing in standard textbooks.
Clinical Epidemiology and Biostatistics by Michael S. Kramer Pdf
Here is a book for clinicians, clinical investigators, trainees, and graduates who wish to develop their proficiency in the planning, execution, and interpretation of clinical and epidemiological research. Emphasis is placed on the design and analysis of research studies involving human subjects where the primary interest concerns principles of analytic (cause-and- effect) inference. The topic is presented from the standpoint of the clinician and assumes no previous knowledge of epidemiology, research design or statistics. Extensive use is made of illustrative examples from a variety of clinical specialties and subspecialties. The book is divided into three parts. Part I deals with epidemiological research design and analytic inference, including such issues as measurement, rates, analytic bias, and the main forms of observational and experimental epidemiological studies. Part II presents the principles and applications of biostatistics, with emphasis on statistical inference. Part III comprises four chapters covering such topics as diagnostic tests, decision analysis, survival (life-table) analysis, and causality.
Statistical Models in Epidemiology by D. Clayton,M. Hills Pdf
This book aims to give a self-contained account of the statistical basis of epidemiology. The book is intended primarily for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics, and should be suitable both as the basis for a taught course and for private study. No previous knowledge is assumed, and the mathematical level has been chosen to suit readers whose basic training is in biology. The most important concept in statistics is the probability model. All statistical analysis of data is based on probability models, even though these may not be explicit. Only by fully understanding the model can one fully understand the analysis. In showing how to use models in epidemiology the authors have chosen to emphasize the role of likelihood. This is an approach to statistics which is both simple and intuitively satisfying, and has the additional advantage that it requires the model and its parameters to be made explicit, even in the simplest situations.
