Methods for Meta-Analysis in Medical Research

Author: A. J. Sutton

Publisher: John Wiley & Sons Incorporated

ISBN: N.A

Category: Mathematics

Page: 317

View: 364

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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.

Research Methods in the Study of Substance Abuse

Author: Jonathan B. VanGeest,Timothy P. Johnson,Sonia A. Alemagno

Publisher: Springer

ISBN: 331955980X

Category: Social Science

Page: 450

View: 5723

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This authoritative handbook reviews the most widely-used methods for studying the use and abuse of alcohol and illegal drugs. Its thorough coverage spans the range of quantitative, qualitative, and mixed-method approaches to documenting and measuring the complex psychological, behavioral, and physical experience of substance misuse and dependence, to ensure valid, useful results. Experts discuss special issues and considerations for conducting ethical research with specialized populations, including youth, inmates, and the LGBT community. Throughout these chapters, contributors demonstrate the multidisciplinary nature of substance abuse research, with emphasis on professional ethics and the critical role of research in developing best practices and effective policy for prevention and treatment. Among the topics covered: · Transdisciplinary research perspective: a theoretical framework for substance abuse research · Longitudinal methods in substance use research · Considerations in blending qualitative and quantitative components in substance abuse research · The use of biological measures in social research on drug misuse · Using surveys to study substance use behavior · Applications of GIS to inform substance abuse research and interventions · Evaluating substance use prevention and treatment programs Research Methods in the Study of Substance Abuse is an essential resource for health services and public health professionals, policymakers, and researchers working and training in the field of addiction. It encourages the rigor and understanding necessary to address widespread social and public health concerns.

Fundamental Issues in Evaluation

Author: Nick L. Smith,Paul R. Brandon

Publisher: Guilford Press

ISBN: 1593853424

Category: Psychology

Page: 266

View: 7369

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Providing state-of-the-art perspectives on what evaluation is, its purpose, and how to ensure it is done well, this book brings together major evaluation researchers from a variety of social and behavioral science disciplines. Each chapter identifies a fundamental issue facing the field today; considers its implications for theory, method, practice, or the profession; and explores one or more approaches to dealing with the issue. Among the topics addressed are the nature of expertise in evaluation, how to build a better evidence base for evaluation theory, promoting cultural competence in evaluation, how to synthesize evaluation research findings, ways to involve stakeholders in decision making, and much more.

The Role of Statistics in Business and Industry

Author: Gerald J. Hahn,Necip Doganaksoy

Publisher: John Wiley & Sons

ISBN: 1118211464

Category: Mathematics

Page: 368

View: 8520

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An insightful guide to the use of statistics for solving key problems in modern-day business and industry This book has been awarded the Technometrics Ziegel Prize for the best book reviewed by the journal in 2010. Technometrics is a journal of statistics for the physical, chemical and engineering sciences, published jointly by the American Society for Quality and the American Statistical Association. Criteria for the award include that the book brings together in one volume a body of material previously only available in scattered research articles and having the potential to significantly improve practice in engineering and science. Highlighting the relevance of statistical methods in everyday applications, The Role of Statistics in Business and Industry bridges the gap between the tools of statistics and their use in today's business world. This one-of-a-kind resource encourages the proactive use of statistics in three well-organized and succinct parts: Setting the Stage provides an introduction to statistics, with a general overview of its uses in business and industry Manufactured Product Applications explains how statistical techniques assist in designing, building, improving, and ensuring the reliability of a wide variety of manufactured products such as appliances, plastic materials, aircraft engines, and locomotives Other Applications describe the role of statistics in pharmaceuticals, finance, and business services, as well as more specialized areas including the food, semiconductor, and communications industries This book is truly unique in that it first describes case studies and key business problems, and then shows how statistics is used to address them, while most literature on the topic does the reverse. This approach provides a comprehensive understanding of common issues and the most effective methods for their treatment. Each chapter concludes with general questions that allow the reader to test their understanding of the presented statistical concepts as well as technical questions that raise more complex issues. An extensive FTP site provides additional material, including solutions to some of the applications. With its accessible style and real-world examples, The Role of Statistics in Business and Industry is a valuable supplement for courses on applied statistics and statistical consulting at the upper-undergraduate and graduate levels. It is also an ideal resource for early-career statisticians and practitioners who would like to learn the value of applying statistics to their everyday work.

Principles of Adult Surgical Critical Care

Author: Niels D. Martin,Lewis J. Kaplan

Publisher: Springer

ISBN: 3319333410

Category: Medical

Page: 569

View: 5360

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This text provides a high level, comprehensive but concise review of adult surgical critical care. It can be used to review complex topics of critical illness in surgical patients, as a reference tool, or as preparation for a board examination. It is focused on the surgical patient including high yield facts, evidence-based guidelines, and critical care principles. To remain succinct, it concentrates on surgically relevant care. Further, the text is written with an expectation that reader already possesses a basic understanding of critical care pathophysiology and clinical practices such as those acquired during residency. Organized by organ system, each section contains several chapters addressing relevant disorders, monitoring and treatment modalities, and outcomes. Principles of Adult Surgical Critical Care will be of use to intensivists caring for surgical patients regardless of parent training domain. Additionally, this work is intended to be used by surgical critical care fellowship trainees as well as other advanced practice providers such as nurse practitioners and physician assistants who provide care in ICUs and emergency departments alike.

Statistical Meta-Analysis with Applications

Author: Joachim Hartung,Guido Knapp,Bimal K. Sinha

Publisher: John Wiley & Sons

ISBN: 1118210964

Category: Medical

Page: 248

View: 4298

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An accessible introduction to performing meta-analysis acrossvarious areas of research The practice of meta-analysis allows researchers to obtainfindings from various studies and compile them to verify and formone overall conclusion. Statistical Meta-Analysis with Applicationspresents the necessary statistical methodologies that allow readersto tackle the four main stages of meta-analysis: problemformulation, data collection, data evaluation, and data analysisand interpretation. Combining the authors' expertise on the topicwith a wealth of up-to-date information, this book successfullyintroduces the essential statistical practices for making thoroughand accurate discoveries across a wide array of diverse fields,such as business, public health, biostatistics, and environmentalstudies. Two main types of statistical analysis serve as the foundationof the methods and techniques: combining tests of effect size andcombining estimates of effect size. Additional topics coveredinclude: Meta-analysis regression procedures Multiple-endpoint and multiple-treatment studies The Bayesian approach to meta-analysis Publication bias Vote counting procedures Methods for combining individual tests and combining individualestimates Using meta-analysis to analyze binary and ordinal categoricaldata Numerous worked-out examples in each chapter provide the readerwith a step-by-step understanding of the presented methods. Allexercises can be computed using the R and SAS software packages,which are both available via the book's related Web site. Extensivereferences are also included, outlining additional sources forfurther study. Requiring only a working knowledge of statistics, StatisticalMeta-Analysis with Applications is a valuable supplement forcourses in biostatistics, business, public health, and socialresearch at the upper-undergraduate and graduate levels. It is alsoan excellent reference for applied statisticians working inindustry, academia, and government.

Meta Analysis in Medicine and Health Policy

Author: Stangl/Berry

Publisher: CRC Press

ISBN: 0824742052

Category: Epidemiology

Page: 414

View: 1700

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A discussion of meta-analysis strategies in medicine and health policy. It builds on sound principles, develops methodologies to solve statistical problems and presents concrete applications used by experienced medical practitioners and health policy-makers. There are numerous open questions.

Statistical Issues in Drug Development

Author: Stephen S. Senn

Publisher: John Wiley & Sons

ISBN: 9780470723579

Category: Medical

Page: 524

View: 3846

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Drug development is the process of finding and producing therapeutically useful pharmaceuticals, turning them into safe and effective medicine, and producing reliable information regarding the appropriate dosage and dosing intervals. With regulatory authorities demanding increasingly higher standards in such developments, statistics has become an intrinsic and critical element in the design and conduct of drug development programmes. Statistical Issues in Drug Development presents an essential and thought provoking guide to the statistical issues and controversies involved in drug development. This highly readable second edition has been updated to include: Comprehensive coverage of the design and interpretation of clinical trials. Expanded sections on missing data, equivalence, meta-analysis and dose finding. An examination of both Bayesian and frequentist methods. A new chapter on pharmacogenomics and expanded coverage of pharmaco-epidemiology and pharmaco-economics. Coverage of the ICH guidelines, in particular ICH E9, Statistical Principles for Clinical Trials. It is hoped that the book will stimulate dialogue between statisticians and life scientists working within the pharmaceutical industry. The accessible and wide-ranging coverage make it essential reading for both statisticians and non-statisticians working in the pharmaceutical industry, regulatory bodies and medical research institutes. There is also much to benefit undergraduate and postgraduate students whose courses include a medical statistics component.

Epidemiological Research Methods

Author: Don McNeil

Publisher: John Wiley & Sons

ISBN: 9780471961963

Category: Mathematics

Page: 305

View: 8106

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The concepts of epidemiology, the science that uses statistical methods to investigate associations between risk factors and disease outcomes in human populations, are developed using examples involving real data from published studies. The relevant statistical methods are developed systematically to provide an integrated approach to observational and experimental studies. After covering basic measurement, study design, and study credibility issues, the author continues with basic statistical methods and techniques for adjusting risk estimates for confounders. Statistical models including logistic regression and the proportional hazards model for survival analysis are explained in detail in the following chapters, concluding with an explanation of the general methods for determining the sample size and power requirements for an epidemiological study. Taking advantage of the power, accessibility and user-friendliness of modern computer packages, the author uses a variety of interesting data sets and graphical displays to illustrate the methods. Epidemiological Research Methods will be of interest to students and research workers who need to learn and appreciate modern approaches to the subject. Without unnecessary emphasis on mathematics or theory, the book will enable the reader to gain a greater level of understanding of the underlying methods than is normally provided in books on epidemiology.

Meta-Analysis of Controlled Clinical Trials

Author: Anne Whitehead

Publisher: John Wiley & Sons

ISBN: 9780471983705

Category: Mathematics

Page: 336

View: 8471

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Over the last twenty years there has been a dramatic upsurge in theapplication of meta-analysis to medical research. This has mainlybeen due to greater emphasis on evidence-based medicine and theneed for reliable summaries of the vast and expanding volume ofclinical research. At the same time there have been great stridesin the development and refinement of the associated statisticalmethodology. This book describes the planning, conduct andreporting of a meta-analysis as applied to a series of randomizedcontrolled clinical trials. * The various approaches are presented within a general unifiedframework. * Meta-analysis techniques are described in detail, from theirtheoretical development through to practical implementation. * Each topic discussed is supported by detailed workedexamples. * A comparison of fixed and random effects approaches is included,as well as a discussion of Bayesian methods and cumulativemeta-analysis. * Fully documented programs using standard statistical proceduresin SAS are available on the Web. Ideally suited for practising statisticians andstatistically-minded medical professionals, the book will also beof use to graduate students of medical statistics. The book is aself-contained and comprehensive account of the subject and anessential purchase for anyone involved in clinical trials.

Statistical Rules of Thumb

Author: Gerald van Belle

Publisher: John Wiley & Sons

ISBN: 1118210360

Category: Mathematics

Page: 304

View: 2090

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Praise for the First Edition: "For a beginner [this book] is a treasure trove; for anexperienced person it can provide new ideas on how better to pursuethe subject of applied statistics." —Journal of Quality Technology Sensibly organized for quick reference, Statistical Rules ofThumb, Second Edition compiles simple rules that arewidely applicable, robust, and elegant, and each captures keystatistical concepts. This unique guide to the use of statisticsfor designing, conducting, and analyzing research studiesillustrates real-world statistical applications through examplesfrom fields such as public health and environmental studies. Alongwith an insightful discussion of the reasoning behind everytechnique, this easy-to-use handbook also conveys the variouspossibilities statisticians must think of when designing andconducting a study or analyzing its data. Each chapter presents clearly defined rules related toinference, covariation, experimental design, consultation, and datarepresentation, and each rule is organized and discussed under fivesuccinct headings: introduction; statement and illustration of therule; the derivation of the rule; a concluding discussion; andexploration of the concept's extensions. The author also introducesnew rules of thumb for topics such as sample size for ratioanalysis, absolute and relative risk, ANCOVA cautions, anddichotomization of continuous variables. Additional features of theSecond Edition include: Additional rules on Bayesian topics New chapters on observational studies and Evidence-BasedMedicine (EBM) Additional emphasis on variation and causation Updated material with new references, examples, and sources A related Web site provides a rich learning environment andcontains additional rules, presentations by the author, and amessage board where readers can share their own strategies anddiscoveries. Statistical Rules of Thumb, SecondEdition is an ideal supplementary book for courses inexperimental design and survey research methods at theupper-undergraduate and graduate levels. It also serves as anindispensable reference for statisticians, researchers,consultants, and scientists who would like to develop anunderstanding of the statistical foundations of their researchefforts. A related website www.vanbelle.org provides additionalrules, author presentations and more.

Clinical Trials

A Methodologic Perspective

Author: Steven Piantadosi

Publisher: Wiley-Interscience

ISBN: N.A

Category: Mathematics

Page: 590

View: 8602

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This book gives the reader important accounts of basic statistical procedures used in clinical trials. It covers several areas of study, including biostatics, biomathematics, biometry and epidemiology. There is emphasis for trialists to learn good methodology while giving quality clinical treatment. Discusses and explores controversial issues such as ethics and offers pragmatic information regarding allegations of fraud or misconduct.

Mixed Models

Theory and Applications with R

Author: Eugene Demidenko

Publisher: John Wiley & Sons

ISBN: 1118091574

Category: Mathematics

Page: 717

View: 5969

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Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. The new edition includes significant updating, over 300 exercises, stimulating chapter projects and model simulations, inclusion of R subroutines, and a revised text format. The target audience continues to be graduate students and researchers. An author-maintained web site is available with solutions to exercises and a compendium of relevant data sets.

Modelling Under Risk and Uncertainty

An Introduction to Statistical, Phenomenological and Computational Methods

Author: Etienne de Rocquigny

Publisher: John Wiley & Sons

ISBN: 1119941652

Category: Mathematics

Page: 416

View: 3756

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Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ? Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the “black-box” view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making. Modelling Under Risk and Uncertainty: Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems. Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events. Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis. Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition. Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding. Supports Master/PhD-level course as well as advanced tutorials for professional training Analysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.

Bayesian statistical modelling

Author: Peter Congdon

Publisher: John Wiley & Sons Inc

ISBN: 9780471496007

Category: Mathematics

Page: 531

View: 9328

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Bayesian methods draw upon previous research findings and combine them with sample data to analyse problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modelling presents an accessible overview of modelling applications from a Bayesian perspective. * Provides an integrated presentation of theory, examples and computer algorithms * Examines model fitting in practice using Bayesian principles * Features a comprehensive range of methodologies and modelling techniques * Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods * Includes extensive applications to health and social sciences * Features a comprehensive collection of nearly 200 worked examples * Data examples and computer code in WinBUGS are available via ftp Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies. Researchers in applied statistics, medical science, public health and the social sciences will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a good reference source for both researchers and students.

Modeling in medical decision making

a Bayesian approach

Author: Giovanni Parmigiani

Publisher: Wiley

ISBN: 9780471986089

Category: Mathematics

Page: 266

View: 8006

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Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to interpret, and can help to address the most pressing practical and ethical concerns arising in medical decision making. * Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory. * Driven by three real applications, presented as extensively detailed case studies. * Case studies include simplified versions of the analysis, to approach complex modelling in stages. * Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling. * Accessible to readers with only a basic statistical knowledge. Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health services research, and health policy.