Author: Yunrong Ye
Publisher:
ISBN:
Category :
Languages : en
Pages : 178

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Author: Peter Diggle
Publisher: Oxford University Press, USA
ISBN: 0199676755
Category : Language Arts & Disciplines
Languages : en
Pages : 397

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Book Description
This second edition has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics. It contains an additional two chapters on fully parametric models for discrete repeated measures data and statistical models for time-dependent predictors.

Author: Garrett Fitzmaurice
Publisher: CRC Press
ISBN: 142001157X
Category : Mathematics
Languages : en
Pages : 633

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Book Description
Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :

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(Uncorrected OCR) Abstract of the thesis entitled Informative Drop-out Models for Longitudinal Binary Data submitted by Chau, Ka Ki for the degree of Master of Philosophy at The University of Hong Kong in December 2003 Attrition or drop-out is a common phenomenon in longitudinal studies in which repeated observations are made on the same subject over time. Subjects always drop out prematurely, especially when the measurement process is lengthy. The problem of drop-out results in incomplete and unbalanced data which in turn results in loss of efficiency and also bias in the analysed results. Regarding this problem, many modeling approaches that deal with missing data have been proposed. This variety of possible approaches differs for different types of drop-out processes and also different kinds of longitudinal data. In this thesis, we aim to develop new modeling strategies for longitudinal binary data with informative drop-out. Three different conditional ARI models are proposed for the response and a logistic regression model for the drop-out process. In these models, both the probabilities of a positive response and the drop-out indicator of a patient in that occasion are assumed to be logit linear in some covariates and outcomes. To account for the problem of over-dispersion and accommodate population het- erogeneity, we incorporate random intercepts to one of the proposed models. We implement the models via likelihood and Bayesian frameworks. Since the inclusion of random effects complicates the calculation considerably, we also attempt to investigate the use of Gibbs output within the Bayesian framework to carry out the Monte Carlo Approximation of the complicated likelihood function involving random effects by a classical likelihood approach. We then demonstrate these models on a methadone clinic data. Moreover, we also investigate the sensitivity of the assumption of the dropout process on the parameter estimates for the three proposed models through simulati.

Author: Lang Wu
Publisher: CRC Press
ISBN: 9781420074086
Category : Mathematics
Languages : en
Pages : 431

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Book Description
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.

Author: Matthew W. Guerra
Publisher:
ISBN:
Category :
Languages : en
Pages : 113

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Author: Michael J. Daniels
Publisher: CRC Press
ISBN: 1420011189
Category : Mathematics
Languages : en
Pages : 324

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Book Description
Drawing from the authors' own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ

Author: Dimitris Rizopoulos
Publisher: CRC Press
ISBN: 1439872864
Category : Mathematics
Languages : en
Pages : 279

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Book Description
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/

Author: Edward F. Vonesh
Publisher: SAS Institute
ISBN: 1629592307
Category : Mathematics
Languages : en
Pages : 529

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Book Description
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. This book is part of the SAS Press program.

Author: Brenden Bishop
Publisher:
ISBN:
Category : Missing observations (Statistics)
Languages : en
Pages : 98

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Book Description
Missing data commonly arise during longitudinal measurements. Dropout is a particular troublesome type of missingness because inference after the dropout occasion is effectively precluded at the level of the individual without substantial assumptions. If missingness, such as dropout, is related to the unobserved outcome variables, then parameter estimates derived from models which ignore the missingness will be biased. For example, a treatment effect may appear less substantial if poor-performing subjects tend to withdraw from the study. In a general sense, missing data lead to scenarios in which the empirical distribution of observed data is lacking nominal coverage in some areas. Little (1993) proposed a general pattern-mixture model approach in which the moments of the full data distribution were estimated as a finite mixture across the various missing-data patterns. These models and their extensions are flexible and may be estimated using wildly available mixed-modeling software in some special cases. The purpose of this work is to review the relevant missing-data literature and to examine the viability of random-coefficient pattern-mixture models as an option for analysts seeking unbiased inference for longitudinal data subject to pernicious dropout.