Download statistical methods for meta analysis in pdf or read statistical methods for meta analysis in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get statistical methods for meta analysis in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Statistical Methods For Meta Analysis

Author: Larry V. Hedges
Publisher: Academic Press
ISBN: 0080570658
Size: 31.29 MB
Format: PDF, Kindle
View: 7070
Download and Read
The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Statistical Methods For Meta Analysis

Author: Larry V. Hedges
Publisher: Academic Press
ISBN:
Size: 79.39 MB
Format: PDF, ePub, Docs
View: 1493
Download and Read
The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns methodology, the content necessarily is statistical, and at times mathematical. In order to make the material accessible to a wider audience, we have not provided proofs in the text. Where proofs are given, they are placed as commentary at the end of a chapter. These can be omitted at the discretion of the reader. Throughout the book we describe computational procedures whenever required. Many computations can be completed on a hand calculator, whereas some require the use of a standard statistical package such as SAS, SPSS, or BMD. Readers with experience using a statistical package or who conduct analyses such as multiple regression or analysis of variance should be able to carry out the analyses described with the aid of a statistical package.

Methods Of Meta Analysis

Author: Frank L. Schmidt
Publisher: SAGE Publications
ISBN: 1483324516
Size: 62.79 MB
Format: PDF
View: 6587
Download and Read
Designed to provide researchers clear and informative insight into techniques of meta-analysis, the Third Edition of Methods of Meta-Analysis: Correcting Error and Bias in Research Findings is the most comprehensive text on meta-analysis available today. It is the only book that presents a full and usable treatment of the role of study artifacts in distorting study results, as well as methods for correcting results for such biases and errors. Meta-analysis is arguably the most important methodological innovation in the last thirty-five years, due to its immense impact on the development of cumulative knowledge and professional practice. This text, now in its updated Third Edition, has been revised to cover the newest developments in meta-analysis methods, evaluation, correction, and more. This reader-friendly book is the definitive resource on meta-analysis. “This text is the primary source text for psychometric meta-analysis methods.” —Emily E. Tanner-Smith, Vanderbilt University “The key strength of the book is the complete and thorough coverage of psychometric meta-analysis. This technique is not covered in any other meta-analysis text, and is a major contribution to the literature…The meta-analysis field needs to find ways to integrate Hunter and Schmidt’s methods into current meta-analysis practice.” —Terri D. Pigott, Loyola University of Chicago “This is an important text. It is the only book that presents adequate coverage of psychometric meta-analysis. In addition to its use as a textbook, it is an invaluable resource for anyone involved in meta-analytic studies.” —Steven Pulos, University of Northern Colorado

Statistical Meta Analysis With Applications

Author: Joachim Hartung
Publisher: John Wiley & Sons
ISBN: 1118210964
Size: 69.57 MB
Format: PDF, ePub, Docs
View: 7597
Download and Read
An accessible introduction to performing meta-analysis across various areas of research The practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies. Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include: 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 individual estimates Using meta-analysis to analyze binary and ordinal categorical data Numerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study. Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.

Statistical Methods For Drug Safety

Author: Robert D. Gibbons
Publisher: CRC Press
ISBN: 1466561858
Size: 56.94 MB
Format: PDF, Kindle
View: 328
Download and Read
Explore Important Tools for High-Quality Work in Pharmaceutical Safety Statistical Methods for Drug Safety presents a wide variety of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for the analysis of spontaneous adverse event reports, and newer approaches, such as the use of marginal structural models for controlling dynamic selection bias in the analysis of large-scale longitudinal observational data. Choose the Right Statistical Approach for Analyzing Your Drug Safety Data The book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis of rare binary adverse events. It explores research involving the re-analysis of complete longitudinal patient records from randomized clinical trials. The book discusses causal inference models, including propensity score matching, marginal structural models, and differential effects, as well as mixed-effects Poisson regression models for analyzing ecological data, such as county-level adverse event rates. The authors also cover numerous other methods useful for the analysis of within-subject and between-subject variation in adverse events abstracted from large-scale medical claims databases, electronic health records, and additional observational data streams. Advance Statistical Practice in Pharmacoepidemiology Authored by two professors at the forefront of developing new statistical methodologies to address pharmacoepidemiologic problems, this book provides a cohesive compendium of statistical methods that pharmacoepidemiologists can readily use in their work. It also encourages statistical scientists to develop new methods that go beyond the foundation covered in the text.

Meta Analysis

Author: Elena Kulinskaya
Publisher: John Wiley & Sons
ISBN: 9780470985526
Size: 14.45 MB
Format: PDF, ePub, Docs
View: 173
Download and Read
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

Statistical Methods In Diagnostic Medicine

Author: Xiao-Hua Zhou
Publisher: John Wiley & Sons
ISBN: 1118626044
Size: 11.42 MB
Format: PDF, Kindle
View: 6468
Download and Read
Praise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.

Meta Analysis Decision Analysis And Cost Effectiveness Analysis

Author: Diana B. Petitti
Publisher: Oxford University Press
ISBN: 0199881111
Size: 22.91 MB
Format: PDF, ePub
View: 4392
Download and Read
Meta-analysis, decision analysis, and cost-effectiveness analysis are the cornerstones of evidence-based medicine. These related quantitative methods have become essential tools in the formulation of clinical and public policy based on the synthesis of evidence. All three methods are taught with increasing frequency in medical schools and schools of public health and in health policy courses at the undergraduate and graduate level. This book is a lucid introduction, and will serve the needs of students taking introductory courses that cover these topics. It will also be useful to clinicians and policymakers who need to understand the quantitative underpinnings of the methods in order to best apply the information that derives from them. The second edition of this popular book adds new material on cumulative meta-analysis as a method to explore heterogeneity. The coverage of cost-effectiveness analysis has been brought into close alignment with recommendations of the U.S. Public Health Panel on Cost-Effectiveness Analysis in Health and Medicine. Many of the examples have been replaced with more current examples, and all of the material has been updated to reflect recent advances in the methods and the emergence of consensus about some previously controversial issues. analysis. These three closely related methods have become even more important for synthesizing research since the first edition was published in 1994. And they have gained legitimacy as tools for guiding health policy.

Meta Analysis

Author: Mike W.-L. Cheung
Publisher: John Wiley & Sons
ISBN: 1119993431
Size: 65.19 MB
Format: PDF, ePub, Mobi
View: 3322
Download and Read
Presents a novel approach to conducting meta–analysis using structural equation modeling. Structural equation modeling (SEM) and meta–analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta–analytic data within the SEM framework, and illustrates how to conduct meta–analysis using the metaSEM package in the R statistical environment. Meta–Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta–analysis in answering research questions. Key ideas in meta–analysis and SEM are briefly reviewed, and various meta–analytic models are then introduced and linked to the SEM framework. Fixed–, random–, and mixed–effects models in univariate and multivariate meta–analyses, three–level meta–analysis, and meta–analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta–analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta–analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta–analysis will be helpful in understanding the materials in this book.