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The explanation and implementation of statistical methods for themedical researcher or statistician remains an integral part ofmodern medical research. This book explains the use of experimentaland analytical biostatistics systems. Its accessible style allowsit to be used by the non-mathematician as a fundamental componentof successful research. Since the third edition, there have been many developments instatistical techniques. The fourth edition provides the medicalstatistician with an accessible guide to these techniques and toreflect the extent of their usage in medical research. The new edition takes a much more comprehensive approach to itssubject. There has been a radical reorganization of the text toimprove the continuity and cohesion of the presentation and toextend the scope by covering many new ideas now being introducedinto the analysis of medical research data. The authors have triedto maintain the modest level of mathematical exposition thatcharacterized the earlier editions, essentially confining themathematics to the statement of algebraic formulae rather thanpursuing mathematical proofs. Received the Highly Commended Certificate in the PublicHealth Category of the 2002 BMA BooksCompetition.
Quantitative Research Methods for Health Professionals: APractical Interactive Course is a superb introductionto epidemiology, biostatistics, and research methodology for thewhole health care community. Drawing examples from a wide range of health research, thispractical handbook covers important contemporary health researchmethods such as survival analysis, Cox regression, andmeta-analysis, the understanding of which go beyond introductoryconcepts. The book includes self-assessment exercises throughout to helpstudents explore and reflect on their understanding and a cleardistinction is made between a) knowledge and concepts that allstudents should ensure they understand and b) those that can bepursued by students who wish to do so. The authors incorporate a program of practical exercises in SPSSusing a prepared data set that helps to consolidate the theory anddevelop skills and confidence in data handling, analysis andinterpretation.
This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.
This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.
- Author : Jue Hou
- Publisher : Unknown
- Release Date : 2019
- Genre : Uncategorized
- Pages : 257
- ISBN : OCLC:1105813323
With the booming of big complex data, various Statistical methods and Data Science techniques have been developed to retrieve valuable information from them. The progress is slower with survival data due to the additional difficulty from censoring and truncation. Except for a few straightforward extensions, most modern learning methods have been absent in survival analysis for years since their invention. The theory on the survival version of those methods also falls further behind. There is a strong demand on computational efficient and theoretical reliable methods for big complex data with time-to-event outcomes in various Health related fields where immense resource has been poured into. This thesis is devoted to incorporating censoring and truncation to state-of-art Statistical methodology and theory, to promote the evolution of survival analysis and support Medical research with up-to-date tools. In Chapter 1, I study the mixture cure-rate model with left truncation and right-censoring. We propose a Nonparametric Maximum Likelihood Estimation (NPMLE) approach to effectively handle the truncation issue. We adopt an efficient and stable EM algorithm. We are able to give a closed form variance estimator giving rise to valid inference. In Chapter 2, I study the estimation and inference for the Fine-Gray competing risks model with high-dimensional covariates. We develop confidence intervals based on a one-step bias-correction to an initial regularized estimator. We lay down a methodological and theoretical framework for the one-step bias-corrected estimator with the partial likelihood. In Chapter 3, I study the inference on treatment effect with censored time-to-event outcome while adjusting for high-dimensional covariates. We propose an orthogonal score method to construct honest confidence intervals for the treatment effect. With a slight modification, we obtain a doubly robust estimator extremely tolerant to both estimation inconsistency and volatility. All the m
Public Health Research Methods, edited by Greg Guest and Emily Namey, provides a comprehensive foundation for planning, executing, and monitoring public health research of all types. The book goes beyond traditional epidemiologic research designs to cover state-of-the-art, technology-based approaches emerging in the new public health landscape. Written by experts in the field, each chapter includes a description of the research method covered, examples of its application in public health, clear instructions on how to execute the method, and a discussion of emerging issues and future directions. In addition, each chapter addresses the topic in the context of global health and health disparities. Such breadth provides readers with practical tools they can use in the field, as well as a current understanding of conceptual discussions. Illustrated with engaging case studies that enhance understanding of the concepts presented, Public Health Research Methods is a comprehensive, must-have reference ideal for researchers in all sectors—government, academia, and non-profit.
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures. The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout. Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.
Focusing on the statistical methods most frequently used in the health care literature and featuring numerous charts, graphs, and up-to-date examples from the literature, this text provides a thorough foundation for the statistics portion of nursing and all health care research courses. All Fifth Edition chapters include new examples and new computer printouts using the latest software, SPSS for Windows, Version 12. New material on regression diagnostics has been added.
- Author : Norou Diawara
- Publisher : Springer
- Release Date : 2019-06-29
- Genre : Mathematics
- Pages : 177
- ISBN : 9783030114312
This contributed volume features invited papers on current models and statistical methods for spatial and multivariate data. With a focus on recent advances in statistics, topics include spatio-temporal aspects, classification techniques, the multivariate outcomes with zero and doubly-inflated data, discrete choice modelling, copula distributions, and feasible algorithmic solutions. Special emphasis is placed on applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the Census dataset for the state of Iowa. Articles use simulated and aggregated data examples to show the flexibility and wide applications of proposed techniques. Carefully peer-reviewed and pedagogically presented for a broad readership, this volume is suitable for graduate and postdoctoral students interested in interdisciplinary research. Researchers in applied statistics and sciences will find this book an important resource on the latest developments in the field. In keeping with the STEAM-H series, the editors hope to inspire interdisciplinary understanding and collaboration.
- Author : Emmanuel Lesaffre
- Publisher : John Wiley & Sons
- Release Date : 2009-03-09
- Genre : Mathematics
- Pages : 408
- ISBN : 047074412X
Statistical and Methodological Aspects of Oral Health Research provides oral health researchers with an overview of the methodological aspects that are important in planning, conducting and analyzing their research projects whilst also providing biostatisticians with an idea of the statistical problems that arise when tackling oral health research questions. This collection presents critical reflections on oral health research and offers advice on practical aspects of setting up research whilst introducing the reader to basic as well as advanced statistical methodology. Features: An introduction to research methodology and an exposition of the state of the art. A variety of examples from oral health research. Contributions from well-known oral health researchers, epidemiologists and biostatisticians, all of whom have rich experience in this area. Recent developments in statistical methodology prompted by a variety of dental applications. Presenting both an introduction to research methodology and an exposition of the latest advances in oral health research, this book will appeal both beginning and experienced oral health researchers as well as biostatisticians and epidemiologists.
- Author : N. Balakrishnan
- Publisher : John Wiley & Sons
- Release Date : 2010-01
- Genre : Mathematics
- Pages : 986
- ISBN : 9780470405093
"Data collection holds an essential part in dictating the future of health sciences and public health, as the compilation of statistics allows researchers and medical practitioners to monitor trends in health status, identify health problems, and evaluate the impact of health policies and programs. Methods and Applications of Statistics in the Life and Health Sciences serves as a single, one-of-a-kind resource on the wide range of statistical methods, techniques, and applications that are applied in modern life and health sciences in research. Specially designed to present encyclopedic content in an accessible and self-contained format, this book outlines thorough coverage of the underlying theory and standard applications to research in related disciplines such as biology, epidemiology, clinical trials, and public health. Uniquely combining established literature with cutting-edge research, this book contains classical works and more than twenty-five new articles and completely revised contributions from the acclaimed Encyclopedia of Statistical Sciences, Second Edition. The result is a compilation of more than eighty articles that explores classic methodology and new topics."--Publisher's description.
- Author : Xiao-Hua Zhou
- Publisher : John Wiley & Sons
- Release Date : 2014-05-19
- Genre : Medical
- Pages : 256
- ISBN : 9781118573648
A modern and practical guide to the essential concepts andideas for analyzing data with missing observations in the field ofbiostatistics With an emphasis on hands-on applications, Applied MissingData Analysis in the Health Sciences outlines the variousmodern statistical methods for the analysis of missing data. Theauthors acknowledge the limitations of established techniques andprovide newly-developed methods with concrete applications in areassuch as causal inference methods and the field of diagnosticmedicine. Organized by types of data, chapter coverage begins with anoverall introduction to the existence and limitations of missingdata and continues into traditional techniques for missing datainference, including likelihood-based, weighted GEE, multipleimputation, and Bayesian methods. The book’s subsequentlycovers cross-sectional, longitudinal, hierarchical, survival data.In addition, Applied Missing Data Analysis in the HealthSciences features: Multiple data sets that can be replicated using the SAS®,Stata®, R, and WinBUGS software packages Numerous examples of case studies in the field of biostatisticsto illustrate real-world scenarios and demonstrate applications ofdiscussed methodologies Detailed appendices to guide readers through the use of thepresented data in various software environments Applied Missing Data Analysis in the Health Sciences isan excellent textbook for upper-undergraduate and graduate-levelbiostatistics courses as well as an ideal resource for healthscience researchers and applied statisticians.
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.
This text enables biomedical researchers to use a number of advanced statistical methods that have proven valuable in medical research, and uses a statistical software package (Stata® ) to avoid mathematics beyond the high school level. Intended for people who have had an introductory course in biostatistics, the volume emphasizes the assumptions underlying each method, using exploratory techniques to determine the most appropriate method. It presents results in a way that will be readily understood by clinical colleagues. Numerous real examples from medical literature and graphical methods are used to illustrate these techniques.
- Author : University of Michigan. Mental Health Research Institute
- Publisher : UM Libraries
- Release Date : 1968
- Genre : Mental health
- Pages : 231
- ISBN : UOM:39015039698819
This is a revised, updated and expanded version of a guide to research skills for psychologists, psychiatrists, nurses, social workers and graduates training in those disciplines.
Foundations of Sleep Health presents sleep health as a critical element of overall individual and population health. Sleep disorders are an increasing problem plaguing more than 40 million Americans. Sleep impacts numerous biological functions and plays a critical role in brain development, including learning and memory consolidation, cognitive functioning, and emotion regulation. This book provides an historic and current overview of the state of sleep health with an emphasis on the interplay between several levels of determinants and factors that influence sleep health. The text provides students in the health professions with in-depth discussion on the theory, research, and practice of sleep health, while also detailing mechanisms, hypotheses, and determinants of sleep and ways to improve sleep health. Discusses the current state of knowledge of sleep health, research into the factors that contribute to and are impacted by sleep health Uses a socioecological model to examine the whole range of determinants of sleep health, from biological to upstream environmental factors and possible modes of intervention Contains a detailed glossary of sleep health terms to aid in the understanding of key concepts Includes learning outcomes for each chapter, objective assessments of knowledge, with explanations, and open-ended questions designed to facilitate discussion
- Author : Janet L. Peacock
- Publisher : Oxford University Press
- Release Date : 2017-07-20
- Genre : Medical
- Pages : 224
- ISBN : 9780198779100
As many medical and healthcare researchers have a love-hate relationship with statistics, the second edition of this practical reference book may make all the difference. Using practical examples, mainly from the authors' own research, the book explains how to make sense of statistics, turn statistical computer output into coherent information, and help decide which pieces of information to report and how to present them. The book takes you through all the stages of the research process, from the initial research proposal, through ethical approval and data analysis, to reporting on and publishing the findings. Helpful tips and information boxes, offer clear guidance throughout, including easily followed instructions on how to: -develop a quantitative research proposal for ethical/institutional approval or research funding -write up the statistical aspects of a paper for publication -choose and perform simple and more advanced statistical analyses -describe the statistical methods and present the results of an analysis. This new edition covers a wider range of statistical programs - SAS, STATA, R, and SPSS, and shows the commands needed to obtain the analyses and how to present it, whichever program you are using. Each specific example is annotated to indicate other scenarios that can be analysed using the same methods, allowing you to easily transpose the knowledge gained from the book to your own research. The principles of good presentation are also covered in detail, from translating relevant results into suitable extracts, through to randomised controlled trials, and how to present a meta-analysis. An added ingredient is the inclusion of code and datasets for all analyses shown in the book on our website (http: //medical-statistics.info). Written by three experienced biostatisticians based in the UK and US, this is a step-by-step guide that will be invaluable to researchers and postgraduate students in medicine, those working in the professions allied to medicin
- Author : Jim Albert
- Publisher : CRC Press
- Release Date : 2017-02-03
- Genre : Mathematics
- Pages : 504
- ISBN : 9781351678964
This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball, football, hockey, basketball, and soccer) followed by a section on other sports and general statistical design and analysis issues that are common to all sports. This handbook has the potential to become the standard reference for obtaining the necessary background to conduct serious statistical analyses for sports applications and to appreciate scholarly work in this expanding area.