Описание: This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars.
Описание: In order to assist a hospital in managing its resources and patients, modelling the length of stay is highly important. Recent health scholarship and practice has largely remained empirical, dwelling on primary data. This is critically important, first, because health planners generally rely on data to establish trends and patterns of disease burden at national or regional level. Secondly, epidemiologists depend on data to investigate possible risk factors of the disease. Yet the use of routine or secondary data has, in recent years, proved increasingly significant in such endeavours. Various units within the health systems collected such data primarily as part of the process for surveillance, monitoring and evaluation. Such data is sometimes periodically supplemented by population-based sample survey datasets. Thirdly, coupled with statistical tools, public health professionals are able to analyze health data and breathe life into what may turn out to be meaningless data. The main focus of this book is to present and showcase advanced modelling of routine or secondary survey data. Studies demonstrate that statistical literacy and knowledge are needed to understand health research outputs. The advent of user-friendly statistical packages combined with computing power and widespread availability of public health data resulted in more reported epidemiological studies in literature. However, analysis of secondary data, has some unique challenges. These are most widely reported health literature, so far has failed to recognize resulting in inappropriate analysis, and erroneous conclusions. This book presents the application of advanced statistical techniques to real examples emanating from routine or secondary survey data. These are essentially datasets in which the two editors have been involved, demonstrating how to tackle these challenges. Some of these challenges are: the complex sampling design of the surveys, the hierarchical nature of the data, the dependence of data at the sampled cluster and missing data among many more challenges. Using data from the Health Management Information System (HMIS), and Demographic and Health Survey (DHS), we provide various approaches and techniques of dealing with data complexity, how to handle correlated or clustered data. Each chapter presents an example code, which can be used to analyze similar data in R, Stata or SPSS. To make the book more concise, we have provided the codes on the books website. The book considers four main topics in the field of health sciences research: (i) structural equation modeling; (ii) spatial and spatio-temporal modeling; (iii) correlated or clustered copula modeling; and (iv) survival analysis. The book has potential to impact methodologists, including students undertaking Masters or Doctoral level programmes as well as other researchers seeking some related reference on quantitative analysis in public health or health sciences or other areas where data of similar nature would be applicable. Further the book can be a resource to public health professionals interested in quantitative approaches to answer questions of epidemiological nature. Each chapter starts with a motivating background, review of statistical methods, analysis and results, ending discussion and possible recommendations.
Автор: Jamie D. Riggs Название: Handbook for Applied Modeling: Non-Gaussian and Correlated Data ISBN: 1316601056 ISBN-13(EAN): 9781316601051 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing data that fail idealized assumptions. It explains and demonstrates core techniques, common pitfalls and data issues, and interpretation of model results, all with a focus on application, utility, and real-life data.
Описание: This proceedings volume contains eight selected papers thatwere presented in the International Symposium in Statistics (ISS) 2015 OnAdvances in Parametric and Semi-parametric Analysis of Multivariate, TimeSeries, Spatial-temporal, and Familial-longitudinal Data, held in St. John`s,Canada from July 6 to 8, 2015.
Описание: Edward Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.
Описание: Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.
Автор: Takeshi Emura; Shigeyuki Matsui; Virginie Rondeau Название: Survival Analysis with Correlated Endpoints ISBN: 981133515X ISBN-13(EAN): 9789811335150 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools.
Автор: Jeffrey R. Wilson; Kent A. Lorenz Название: Modeling Binary Correlated Responses using SAS, SPSS and R ISBN: 3319373617 ISBN-13(EAN): 9783319373614 Издательство: Springer Рейтинг: Цена: 11878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistical tools to analyze correlated binary data are spread out in the existing literature. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book.
Автор: Timothy G. Gregoire; David R. Brillinger; Peter Di Название: Modelling Longitudinal and Spatially Correlated Data ISBN: 0387982167 ISBN-13(EAN): 9780387982168 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This refereed volume includes papers presented at a conference on modelling longitudinal and spatially correlated data. Many of the best researchers in the world have presented papers in an area with important applications to biostatistics and the environmental sciences.
Автор: Wicher Bergsma; Marcel A. Croon; Jacques A. Hagena Название: Marginal Models ISBN: 1441918736 ISBN-13(EAN): 9781441918734 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Marginal models are often the best way of answering research questions involving dependent observations. This comprehensive overview of the basic principles of marginal modeling offers a wide range of possible applications through many real world examples.
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