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Power Analysis Of Trials With Multilevel Data
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Book Synopsis Power Analysis of Trials with Multilevel Data by : Mirjam Moerbeek
Download or read book Power Analysis of Trials with Multilevel Data written by Mirjam Moerbeek and published by CRC Press. This book was released on 2015-07-01 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Power Analysis of Trials with Multilevel Data is a valuable reference for anyone who wants to perform power calculations on trials with hierarchical data. It provides a thorough overview of power analysis, familiarizing you with terminology and notation, outlining the key concepts of statistical power and power analysis, and covering all common hierarchical designs.
Book Synopsis Doing Meta-Analysis with R by : Mathias Harrer
Download or read book Doing Meta-Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Book Synopsis Practical Statistical Power Analysis by : Zhiyong Zhang
Download or read book Practical Statistical Power Analysis written by Zhiyong Zhang and published by Isdsa Press. This book was released on 2018-05 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of conducting a statistical power analysis at the beginning of a study is universally accepted. Without careful planning, a study can easily fail to detect an existing effect by chance. This book teaches how to conduct power analysis for a range of models from correlation and t-test to structural equation models and multilevel models. Both a graphical Web software WebPower and an R package are introduced to practically conduct the analysis.
Book Synopsis Multilevel Analysis by : Tom A. B. Snijders
Download or read book Multilevel Analysis written by Tom A. B. Snijders and published by SAGE. This book was released on 1999 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multilevel analysis covers all the main methods, techniques and issues for carrying out multilevel modeling and analysis. The approach is applied, and less mathematical than many other textbooks.
Book Synopsis Statistical Power Analysis by : Kevin R. Murphy
Download or read book Statistical Power Analysis written by Kevin R. Murphy and published by Routledge. This book was released on 2003-08-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a simple and general method for conducting statistical power analysis based on the widely used F statistic. The book illustrates how these analyses work and how they can be applied to problems of studying design, to evaluate others' research, and to choose the appropriate criterion for defining "statistically significant" outcomes. Statistical Power Analysis examines the four major applications of power analysis, concentrating on how to determine: *the sample size needed to achieve desired levels of power; *the level of power that is needed in a study; *the size of effect that can be reliably detected by a study; and *sensible criteria for statistical significance. Highlights of the second edition include: a CD with an easy-to-use statistical power analysis program; a new chapter on power analysis in multi-factor ANOVA, including repeated-measures designs; and a new One-Stop PV Table to serve as a quick reference guide. The book discusses the application of power analysis to both traditional null hypothesis tests and to minimum-effect testing. It demonstrates how the same basic model applies to both types of testing and explains how some relatively simple procedures allow researchers to ask a series of important questions about their research. Drawing from the behavioral and social sciences, the authors present the material in a nontechnical way so that readers with little expertise in statistical analysis can quickly obtain the values needed to carry out the power analysis. Ideal for students and researchers of statistical and research methodology in the social, behavioral, and health sciences who want to know how to apply methods of power analysis to their research.
Book Synopsis Introduction to Power Analysis by : E. C. Hedberg
Download or read book Introduction to Power Analysis written by E. C. Hedberg and published by SAGE Publications. This book was released on 2017-12-05 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Power Analysis: Two-Group Studies provides readers with the background, examples, and explanation they need to read technical papers and materials that include complex power analyses. This clear and accessible guide explains the components of test statistics and their sampling distributions, and author Eric Hedberg walks the reader through the simple and complex considerations of this research question. Filled with graphics and examples, the reader is taken on a tour of power analyses from covariates to clusters, seeing how the complicated task of comparing two groups, and the power analysis, can be made easy.
Book Synopsis Statistical Power Analysis with Missing Data by : Adam Davey
Download or read book Statistical Power Analysis with Missing Data written by Adam Davey and published by Routledge. This book was released on 2009-08-20 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.
Book Synopsis Handbook of Regression Modeling in People Analytics by : Keith McNulty
Download or read book Handbook of Regression Modeling in People Analytics written by Keith McNulty and published by CRC Press. This book was released on 2021-07-29 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.
Book Synopsis Design and Analysis of Group-randomized Trials by : David M. Murray
Download or read book Design and Analysis of Group-randomized Trials written by David M. Murray and published by Monographs in Epidemiology and. This book was released on 1998 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Community or group-randomized trials, which are usually done to evaluate the effect of health promotion effors. It reviews the underlying issues, describes the most widely used research design, and presents the many approaches to analysis that are now available.
Book Synopsis Small Sample Size Solutions by : Rens van de Schoot
Download or read book Small Sample Size Solutions written by Rens van de Schoot and published by Routledge. This book was released on 2020-02-13 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.
Book Synopsis Small Clinical Trials by : Institute of Medicine
Download or read book Small Clinical Trials written by Institute of Medicine and published by National Academies Press. This book was released on 2001-01-01 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.
Book Synopsis Introducing Multilevel Modeling by : Ita G G Kreft
Download or read book Introducing Multilevel Modeling written by Ita G G Kreft and published by SAGE. This book was released on 1998-04-07 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming increasingly important in social, behavioural, and educational research and it is clear from recent developments that such models are seen as being more realistic, and potentially more revealing, than ordinary regression models. While other books describe these multilevel models in considerable detail none focuses on the practical issues and potential problems of doing multilevel analyses that are covered in Introducing Multilevel Modeling. The authors' approach is user-oriented and the formal mathematics and statistics are kept to a minimum. Other key features include the use of worked examples using real data sets, analyzed using the leading computer package for multilevel modeling - "MLn." Discussion site at: http: \www.stat.ucla.eduphplibw-agoraw-agora.phtml?bn=Sagebook Data files mentioned in the book are available from: http: \www.stat.ucla.edu deleeuwsagebook
Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Book Synopsis Statistical Methods in Psychiatry and Related Fields by : Ralitza Gueorguieva
Download or read book Statistical Methods in Psychiatry and Related Fields written by Ralitza Gueorguieva and published by CRC Press. This book was released on 2017-11-20 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data collected in psychiatry and related fields are complex because outcomes are rarely directly observed, there are multiple correlated repeated measures within individuals, there is natural heterogeneity in treatment responses and in other characteristics in the populations. Simple statistical methods do not work well with such data. More advanced statistical methods capture the data complexity better, but are difficult to apply appropriately and correctly by investigators who do not have advanced training in statistics. This book presents, at a non-technical level, several approaches for the analysis of correlated data: mixed models for continuous and categorical outcomes, nonparametric methods for repeated measures and growth mixture models for heterogeneous trajectories over time. Separate chapters are devoted to techniques for multiple comparison correction, analysis in the presence of missing data, adjustment for covariates, assessment of mediator and moderator effects, study design and sample size considerations. The focus is on the assumptions of each method, applicability and interpretation rather than on technical details. Features Provides an overview of intermediate to advanced statistical methods applied to psychiatry. Takes a non-technical approach with mathematical details kept to a minimum. Includes lots of detailed examples from published studies in psychiatry and related fields. Software programs, data sets and output are available on a supplementary website. The intended audience are applied researchers with minimal knowledge of statistics, although the book could also benefit collaborating statisticians. The book, together with the online materials, is a valuable resource aimed at promoting the use of appropriate statistical methods for the analysis of repeated measures data. Ralitza Gueorguieva is a Senior Research Scientist at the Department of Biostatistics, Yale School of Public Health. She has more than 20 years experience in statistical methodology development and collaborations with psychiatrists and other researchers, and is the author of over 130 peer-reviewed publications.
Book Synopsis Handbook of Multilevel Analysis by : Jan Deleeuw
Download or read book Handbook of Multilevel Analysis written by Jan Deleeuw and published by Springer Science & Business Media. This book was released on 2007-12-26 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.
Book Synopsis Multilevel Analysis by : Joop J. Hox
Download or read book Multilevel Analysis written by Joop J. Hox and published by Routledge. This book was released on 2017-09-14 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.
Book Synopsis Modern Directional Statistics by : Christophe Ley
Download or read book Modern Directional Statistics written by Christophe Ley and published by CRC Press. This book was released on 2017-08-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.
