Mixed Effects Models for the Population Approach

Author: Marc Lavielle
Publisher: CRC Press
ISBN: 1482226502
Format: PDF, ePub, Mobi
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Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data Modeling The book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical Implementation This book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.

Mobilit tsbiografien und Mobilit tssozialisation

Author: Lisa Döring
Publisher: Springer-Verlag
ISBN: 3658228253
Format: PDF, Kindle
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Lisa Döring untersucht Mobilitätsbiografien von 1911 bis 2000 Geborenen mittels Standard-Kohorten-Tabellen und Markov Modellen. Die Ergebnisse zeigen Veränderungen im Mobilitätshandeln. Sie bestätigen nicht den häufig postulierten Bedeutungsverlust des Pkw unter jungen Menschen, sondern zeigen, dass zwischen 1951-1960 Geborene im Vergleich zu allen anderen Altersgruppen im besonderen Maße autoorientiert sind. Entlang der Biographien werden, von älteren zu jüngeren Geburtsjahrgängen, eine Standardisierung und eine Homogenisierung von Mobilitätsbiografien bezüglich des Führerscheinerwerbs, der Autoverfügbarkeit und der Verkehrsmittelnutzung deutlich. Hinsichtlich der Pkw-Verfügbarkeit und Verkehrsmittelnutzung auf Berufswegen wird kein generationsübergreifender familiärer Zusammenhang nachgewiesen.

Mixed Effects Models for Complex Data

Author: Lang Wu
Publisher: CRC Press
ISBN: 9781420074086
Format: PDF, ePub, Docs
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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.

Evaluation of Statistical Matching and Selected SAE Methods

Author: Verena Puchner
Publisher: Springer
ISBN: 3658082240
Format: PDF, ePub, Mobi
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Verena Puchner evaluates and compares statistical matching and selected SAE methods. Due to the fact that poverty estimation at regional level based on EU-SILC samples is not of adequate accuracy, the quality of the estimations should be improved by additionally incorporating micro census data. The aim is to find the best method for the estimation of poverty in terms of small bias and small variance with the aid of a simulated artificial "close-to-reality" population. Variables of interest are imputed into the micro census data sets with the help of the EU-SILC samples through regression models including selected unit-level small area methods and statistical matching methods. Poverty indicators are then estimated. The author evaluates and compares the bias and variance for the direct estimator and the various methods. The variance is desired to be reduced by the larger sample size of the micro census.

Analyzing and Modeling Rank Data

Author: John I Marden
Publisher: CRC Press
ISBN: 9780412995217
Format: PDF, Mobi
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This book is the first single source volume to fully address this prevalent practice in both its analytical and modeling aspects. The information discussed presents the use of data consisting of rankings in such diverse fields as psychology, animal science, educational testing, sociology, economics, and biology. This book systematically presents the basic models and methods for analyzing data in the form of ranks. Integrating material from a wide range of fields, this book applies graphical, numerical, and modeling techniques to data sets, uncovering fascinating structures in the rank data. Providing the most extensive coverage of the subject found in statistical literature, this book will be a welcomed reference to statisticians. In addition, this volume is also accessible to people in all areas of quantitative research. Researchers in psychology and consumer preference will discover a valuable resource; and sociologists, biologists, political and animal scientists will also benefit. As a text, it will be ideal for graduate students in courses on statistics and other quantitative disciplines.

Handbook of Health Survey Methods

Author: Timothy P. Johnson
Publisher: John Wiley & Sons
ISBN: 1118594746
Format: PDF, ePub, Mobi
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A comprehensive guidebook to the current methodologiesand practices used in health surveys A unique and self-contained resource, Handbook of HealthSurvey Methods presents techniques necessary for confrontingchallenges that are specific to health survey research. Thehandbook guides readers through the development of sample designs,data collection procedures, and analytic methods for studies aimedat gathering health information on general and targetedpopulations. The book is organized into five well-defined sections: Design andSampling Issues, Measurement Issues, Field Issues, Health Surveysof Special Populations, and Data Management and Analysis.Maintaining an easy-to-follow format, each chapter begins with anintroduction, followed by an overview of the main concepts,theories, and applications associated with each topic. Finally,each chapter provides connections to relevant online resources foradditional study and reference. The Handbook of Health SurveyMethods features: 29 methodological chapters written by highly qualified expertsin academia, research, and industry A treatment of the best statistical practices and specificmethodologies for collecting data from special populations such assexual minorities, persons with disabilities, patients, andpractitioners Discussions on issues specific to health research includingdeveloping physical health and mental health measures, collectinginformation on sensitive topics, sampling for clinical trials,collecting biospecimens, working with proxy respondents, andlinking health data to administrative and other external datasources Numerous real-world examples from the latest research in thefields of public health, biomedicine, and health psychology Handbook of Health Survey Methods is an ideal reference foracademics, researchers, and practitioners who apply survey methodsand analyze data in the fields of biomedicine, public health,epidemiology, and biostatistics. The handbook is also a usefulsupplement for upper-undergraduate and graduate-level courses onsurvey methodology.

Topics in Modelling of Clustered Data

Author: Marc Aerts
Publisher: CRC Press
ISBN: 1420035886
Format: PDF, ePub, Docs
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Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods. The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The applications discussed center primarily, but not exclusively, on developmental toxicity, which leads naturally to discussion of other methodologies, including risk assessment and dose-response modelling. Clearly written, Topics in Modelling of Clustered Data offers a practical, easily accessible survey of important modelling issues. Overview models give structure to a multitude of approaches, figures help readers visualize model characteristics, and a generous use of examples illustrates all aspects of the modelling process.

Analysis of Binary Data Second Edition

Author: D.R. Cox
Publisher: CRC Press
ISBN: 9780412306204
Format: PDF, ePub, Docs
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The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods. There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.

Multistate Models for the Analysis of Life History Data

Author: Richard J Cook
Publisher: CRC Press
ISBN: 1351646052
Format: PDF, ePub, Docs
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Multistate Models for the Analysis of Life History Data provides the first comprehensive treatment of multistate modeling and analysis, including parametric, nonparametric and semiparametric methods applicable to many types of life history data. Special models such as illness-death, competing risks and progressive processes are considered, as well as more complex models. The book provides both theoretical development and illustrations of analysis based on data from randomized trials and observational cohort studies in health research. Features Discusses a wide range of applications of multistate models Presents methods for both continuously and intermittently observed life history processes Gives a thorough discussion of conditionally independent censoring and observation processes Discusses models with random effects and joint models for two or more multistate processes Discusses and illustrates software for multistate analysis that is available in R Target audience includes those engaged in research and applications involving multistate models Richard Cook is Canada Research Chair in Statistical Methods for Health Research at the University of Waterloo. He has received the Gold Medal of the Statistical Society of Canada and is a Fellow of the American Statistical Association. He collaborates and consults widely on health research and has given many short courses. He and Dr. Lawless previously coauthored the influential book, The Statistical Analysis of Recurrent Events (Springer, 2007). Jerald Lawless is Distinguished Professor Emeritus at the University of Waterloo. He is a Fellow of the Royal Society of Canada, a Gold Medal recipient of the Statistical Society of Canada and Fellow of the American Statistical Association. He is a past editor of Technometrics and has collaborated and consulted in numerous areas. He has presented many short courses, with Dr. Cook and individually. "The authors of the book are internationally renowned experts in the field of multi-state modeling and have written an extremely clear and comprehensive book on the topic that covers many different aspects, from the fundamental theory to the practical side of analyzing data and interpreting results. The examples are well chosen to represent the most common types of multi-state processes that public health researchers could encounter. The inclusion of software code to illustrate how the models can be fit and interpreted is especially helpful to readers." (Mimi Kim, Albert Einstein College of Medicine)