Power Analysis of Trials with Multilevel Data PDF Download

Author: Mirjam Moerbeek
Publisher: CRC Press
ISBN: 1498729908
Category : Mathematics
Languages : en
Pages : 288

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Book Description
Power Analysis of Trials with Multilevel Data covers using power and sample size calculations to design trials that involve nested data structures. The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial de

Author: E. C. Hedberg
Publisher: SAGE Publications
ISBN: 1506343139
Category : Social Science
Languages : en
Pages : 176

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Book Description
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.

Author: Mathias Harrer
Publisher: CRC Press
ISBN: 1000435636
Category : Mathematics
Languages : en
Pages : 500

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Book Description
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

Author: Ann A. O'Connell
Publisher: IAP
ISBN: 164802873X
Category : Education
Languages : en
Pages : 645

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Book Description
Multilevel Modeling Methods with Introductory and Advanced Applications provides a cogent and comprehensive introduction to the area of multilevel modeling for methodological and applied researchers as well as advanced graduate students. The book is designed to be able to serve as a textbook for a one or two semester course in multilevel modeling. The topics of the seventeen chapters range from basic to advanced, yet each chapter is designed to be able to stand alone as an instructional unit on its respective topic, with an emphasis on application and interpretation. In addition to covering foundational topics on the use of multilevel models for organizational and longitudinal research, the book includes chapters on more advanced extensions and applications, such as cross-classified random effects models, non-linear growth models, mixed effects location scale models, logistic, ordinal, and Poisson models, and multilevel mediation. In addition, the volume includes chapters addressing some of the most important design and analytic issues including missing data, power analyses, causal inference, model fit, and measurement issues. Finally, the volume includes chapters addressing special topics such as using large-scale complex sample datasets, and reporting the results of multilevel designs. Each chapter contains a section called Try This!, which poses a structured data problem for the reader. We have linked our book to a website (http://modeling.uconn.edu) containing data for the Try This! section, creating an opportunity for readers to learn by doing. The inclusion of the Try This! problems, data, and sample code eases the burden for instructors, who must continually search for class examples and homework problems. In addition, each chapter provides recommendations for additional methodological and applied readings.

Author: Zhiyong Zhang
Publisher: Isdsa Press
ISBN: 9781946728029
Category :
Languages : en
Pages : 358

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Book Description
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.

Author: Joop J. Hox
Publisher: Routledge
ISBN: 1317308689
Category : Psychology
Languages : en
Pages : 348

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Book Description
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.

Author: Linda M. Collins
Publisher: Springer
ISBN: 3319917765
Category : Social Science
Languages : en
Pages : 301

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Book Description
Behavioral, biobehavioral, and biomedical interventions are programs with the objective of improving and maintaining human health and well-being, broadly defined, in individuals, families, schools, organizations, or communities. These interventions may be aimed at, for example, preventing or treating disease, promoting physical and mental health, preventing violence, or improving academic achievement. This book provides additional information on a principled empirical framework for developing interventions that are more effective, efficient, economical, and scalable. This framework is introduced in the monograph, "Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST)" by Linda M. Collins (Springer, 2018). The present book is focused on advanced topics related to MOST. The chapters, all written by experts, are devoted to topics ranging from experimental design and data analysis to development of a conceptual model and implementation of a complex experiment in the field. Intervention scientists who are preparing to apply MOST will find this book an important reference and guide for their research. Fields to which this work pertains include public health (medicine, nursing, health economics, implementation sciences), behavioral sciences (psychology, criminal justice), statistics, and education.

Author: Song Zhang
Publisher: CRC Press
ISBN: 1000873552
Category : Medical
Languages : en
Pages : 215

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Book Description
This book begins with an introduction of pragmatic cluster randomized trials (PCTs) and reviews various pragmatic issues that need to be addressed by statisticians at the design stage. It discusses the advantages and disadvantages of each type of PCT, and provides sample size formulas, sensitivity analyses, and examples for sample size calculation. The generalized estimating equation (GEE) method will be employed to derive sample size formulas for various types of outcomes from the exponential family, including continuous, binary, and count variables. Experimental designs that have been frequently employed in PCTs will be discussed, including cluster randomized designs, matched-pair cluster randomized design, stratified cluster randomized design, stepped-wedge cluster randomized design, longitudinal cluster randomized design, and crossover cluster randomized design. It demonstrates that the GEE approach is flexible to accommodate pragmatic issues such as hierarchical correlation structures, different missing data patterns, randomly varying cluster sizes, etc. It has been reported that the GEE approach leads to under-estimated variance with limited numbers of clusters. The remedy for this limitation is investigated for the design of PCTs. This book can assist practitioners in the design of PCTs by providing a description of the advantages and disadvantages of various PCTs and sample size formulas that address various pragmatic issues, facilitating the proper implementation of PCTs to improve health care. It can also serve as a textbook for biostatistics students at the graduate level to enhance their knowledge or skill in clinical trial design. Key Features: Discuss the advantages and disadvantages of each type of PCTs, and provide sample size formulas, sensitivity analyses, and examples. Address an unmet need for guidance books on sample size calculations for PCTs; A wide variety of experimental designs adopted by PCTs are covered; The sample size solutions can be readily implemented due to the accommodation of common pragmatic issues encountered in real-world practice; Useful to both academic and industrial biostatisticians involved in clinical trial design; Can be used as a textbook for graduate students majoring in statistics and biostatistics.

Author: Susan Halabi
Publisher: CRC Press
ISBN: 1351620967
Category : Medical
Languages : en
Pages : 701

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Book Description
There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology. Key Features: Cutting-edge topics with appropriate technical background Built around case studies which give the work a "hands-on" approach Real examples of flaws in previously reported clinical trials and how to avoid them Access to statistical code on the book’s website Chapters written by internationally recognized statisticians from academia and pharmaceutical companies Carefully edited to ensure consistency in style, level, and approach Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others

Author: Alastair H. Leyland
Publisher: Springer Nature
ISBN: 3030348016
Category : Medical
Languages : en
Pages : 293

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Book Description
This open access book is a practical introduction to multilevel modelling or multilevel analysis (MLA) – a statistical technique being increasingly used in public health and health services research. The authors begin with a compelling argument for the importance of researchers in these fields having an understanding of MLA to be able to judge not only the growing body of research that uses it, but also to recognise the limitations of research that did not use it. The volume also guides the analysis of real-life data sets by introducing and discussing the use of the multilevel modelling software MLwiN, the statistical package that is used with the example data sets. Importantly, the book also makes the training material accessible for download – not only the datasets analysed within the book, but also a freeware version of MLwiN to allow readers to work with these datasets. The book’s practical review of MLA comprises: Theoretical, conceptual, and methodological background Statistical background The modelling process and presentation of research Tutorials with example datasets Multilevel Modelling for Public Health and Health Services Research: Health in Context is a practical and timely resource for public health and health services researchers, statisticians interested in the relationships between contexts and behaviour, graduate students across these disciplines, and anyone interested in utilising multilevel modelling or multilevel analysis. “Leyland and Groenewegen’s wealth of teaching experience makes this book and its accompanying tutorials especially useful for a practical introduction to multilevel analysis.” ̶ Juan Merlo, Professor of Social Epidemiology, Lund University “Comprehensive and insightful. A must for anyone interested in the applications of multilevel modelling to population health”. ̶ S. (Subu) V. Subramanian, Professor of Population Health and Geography, Harvard University