Nonparametric Analysis Of Longitudinal Data In Factorial Experiments
Download Nonparametric Analysis Of Longitudinal Data In Factorial Experiments PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Nonparametric Analysis Of Longitudinal Data In Factorial Experiments book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Nonparametric Analysis of Longitudinal Data in Factorial Experiments
The authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach. Emphasizing the practical application of these methods in statistical procedures, this book provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data. Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points. The basic mathematical principles for the methods introduced here are described in theory, consistent with the book's minimal math requirements. Simple approximations for small data sets are provided, as well as ample chapter exercises to test skills, an appendix that includes original data for the examples used throughout the book, and downloadable SAS-IML macros for implementing the more extensive calculations. All applications are designed to be useful in many fields. Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics.
Applied Survival Analysis
Author: David W. Hosmer, Jr.
language: en
Publisher: John Wiley & Sons
Release Date: 2011-09-23
THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.
Bayesian Statistical Modelling
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology