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This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis. Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.
In radiation oncology as in many other specialties clinical trials are essential to investigate new therapy approaches. Usually, preparation for a prospective clinical trial is extremely time consuming until ethics approval is obtained. To test a new treatment usually many years pass before it can be implemented in the routine care. During that time, already new interventions emerge, new drugs appear on the market, technical & physical innovations are being implemented, novel biology driven concepts are translated into clinical approaches while we are still investigating the ones from years ago. Another problem is associated with molecular diagnostics and the growing amount of tumor specific biomarkers which allows for a better stratification of patient subgroups. On the other side, this may result in a much longer time for patient recruiting and consequently in larger multicenter trials. Moreover, all of the relevant data must be readily available for treatment decision making, treatment as well as follow-up, and ultimately for trial evaluation. This challenges even more for agreed standards in data acquisition, quality and management. How could we change the way currently clinical trials are performed in a way they are safe and ethically justifiable and speed up the initiation process, so we can provide new and better treatments faster for our patients? Further, while we rely on various quantitative information handling distributed, large heterogeneous amounts of data efficiently is very important. Thus data management becomes a strong focus. A good infrastructure helps to plan, tailor and conduct clinical trials in a way they are easy and quickly analyzable. In this research topic we want to discuss new ideas for intelligent trial designs and concepts for data management.
The Second Edition of An Applied Guide to Research Designs offers researchers in the social and behavioral sciences guidance for selecting the most appropriate research design to apply in their study. Using consistent terminology, authors W. Alex Edmonds and Thomas D. Kennedy visually present a range of research designs used in quantitative, qualitative, and mixed methods to help readers conceptualize, construct, test, and problem solve in their investigation. The Second Edition features revamped and expanded coverage of research designs, new real-world examples and references, a new chapter on action research, and updated ancillaries.
Statistical analysis typically involves applying theoretically generated techniques to the description and interpretation of collected data. In this text, theory, application and interpretation are combined to present the entire biostatistical process for a series of elementary and intermediate analytic methods. The theoretical basis for each method is discussed with a minimum of mathematics and is applied to a research data example using a computer system called S-PLUS. This system produces concrete numerical results and increases one's understanding of the fundamental concepts and methodology of statistical analysis. Combining statistical logic, data and computer tools, the author explores such topics as random number generation, general linear models, estimation, analysis of tabular data, analysis of variance and survival analysis. The end result is a clear and complete explanation of the way statistical methods can help one gain an understanding of collected data. Modern Applied Biostatistical Methods is unlike other statistical texts, which usually deal either with theory or with applications. It integrates the two elements into a single presentation of theoretical background, data, interpretation, graphics, and implementation. This all-around approach will be particularly helpful to students in various biostatistics and advanced epidemiology courses, and will interest all researchers involved in biomedical data analysis. This text is not a computer manual, even though it makes extensive use of computer language to describe and illustrate applied statistical techniques. This makes the details of the statistical process readily accessible, providing insight into how and why a statistical method identifies the properties of sampled data. The first chapter gives a simple overview of the S-PLUS language. The subsequent chapters use this valuable statistical tool to present a variety of analytic approaches.
Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.
Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. It provides both theoretical background and practical solutions for those working with incomplete data sets in an engaging example-driven format.
Featuring articles from the prestigious Encyclopedia of Biostatistics, many of which have been revised and updated to include recent developments, the Encyclopedia of Epidemiologic Methods also includes newly commissioned articles reflecting the latest thinking in Cancer Registries Birth Defect Registries Meta Analysis of Epidemiologic Studies Epidemiology Overview Sample Size Sex Ratio at Birth Software Design and Analysis Featuring contributions from leading experts in academia, government and industry, the Encyclopedia of Epidemiologic Methods has been designed to complement existing texts on the subject by providing further extensive, up-to-date coverage of specialised topics and by introducing the reader to the research literature. Offering a wealth of information in a single resource, the Encyclopedia of Epidemiologic Methods Offers an excellent introduction to a vast array of specialised topics Includes in-depth coverage of the statistical underpinnings of contemporary epidemiologic methods Provides concise definitions and introductions to numerous concepts found in the current literature Uses extensive cross-references, helping to facilitate further research, and enabling the reader to locate definitions and related concepts In addition to featuring extensive articles in the areas of descriptive and analytic epidemiology, the Encyclopedia also provides the reader with articles on case-control design and offers substantial coverage of allied statistical methods.
Become a successful evidence-based practitioner. How do you evaluate the evidence? Is the information accurate, relevant and meaningful for clinical decision making? Did the design fit the research questions and was the analysis and interpretation of data appropriate? Here are all the materials you need to take your first steps as evidence-based practitioners…how to use the design, data and analysis of research as the foundation for effective clinical decision making. You’ll find support every step of the way as you progress from the foundations of clinical research and concepts of measurement through the processes of designing studies and analyzing data to writing their own research proposal.