A Joint Model of Longitudinal Data and Informative Time with Time-dependent Covariate PDF Download

Author: Mohammad Abdullatif Alomair
Publisher:
ISBN:
Category : Longitudinal method
Languages : en
Pages : 240

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Author: Robert Elashoff
Publisher: CRC Press
ISBN: 1439807833
Category : Mathematics
Languages : en
Pages : 262

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Book Description
Longitudinal studies often incur several problems that challenge standard statistical methods for data analysis. These problems include non-ignorable missing data in longitudinal measurements of one or more response variables, informative observation times of longitudinal data, and survival analysis with intermittently measured time-dependent covariates that are subject to measurement error and/or substantial biological variation. Joint modeling of longitudinal and time-to-event data has emerged as a novel approach to handle these issues. Joint Modeling of Longitudinal and Time-to-Event Data provides a systematic introduction and review of state-of-the-art statistical methodology in this active research field. The methods are illustrated by real data examples from a wide range of clinical research topics. A collection of data sets and software for practical implementation of the joint modeling methodologies are available through the book website. This book serves as a reference book for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this field. It may also be used as a textbook for a graduate level course in biostatistics or statistics.

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: Joseph G. Ibrahim
Publisher: Springer Science & Business Media
ISBN: 1475734476
Category : Medical
Languages : en
Pages : 494

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Book Description
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

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: 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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Book Description
In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Interest may focus on inference on the longitudinal data process, which is informatively censored by death or withdrawal, or on the hazard relationship. Several methods for fitting such models have been proposed, including regression calibration and likelihood or Bayesian methods. However, most approaches require a parametric distributional assumption (normality) on the random effects. In addition, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. We extend this technique to multiple, possibly correlated, time-dependent covariates. This approach is easy to compute. However, the conditional score approach might be less efficient relative to the likelihood approaches. In addition, inference on the longitudinal data process is not available. To improve the efficiency and meanwhile obtain an estimator for the random effects distribution, we propose to approximate the random effects distribution by the seminonparametric (SNP) densities of Gallant and Nychka (1987), which requires only the assumption that the random effects have a "smooth" density, and take a semiparametric likelihood approach. The EM algorithm is used for implementation. We demonstrate the approaches via simulations and apply th.

Author: Robert E. Weiss
Publisher: Springer Science & Business Media
ISBN: 0387283145
Category : Medical
Languages : en
Pages : 445

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Book Description
The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.

Author: Susan J Henly
Publisher: Routledge
ISBN: 113470030X
Category : Medical
Languages : en
Pages : 589

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Book Description
Designed to support global development of nursing science, the Routledge International Handbook of Advanced Quantitative Methods in Nursing Research provides a new, comprehensive, and authoritative treatment of advanced quantitative methods for nursing research. Incorporating past approaches that have served as the foundation for the science, this cutting edge book also explores emerging approaches that will shape its future. Divided into six parts, it covers: -the domain of nursing science - measurement—classical test theory, IRT, clinimetrics, behavioral observation, biophysical measurement -models for prediction and explanation—SEM, general growth mixture models, hierarchical models, analysis of dynamic systems -intervention research—theory-based interventions, causality, third variables, pilot studies, quasi-experimental design, joint models for longitudinal data and time to event -e-science—DIKW paradigm, big data, data mining, omics, FMRI -special topics—comparative effectiveness and meta-analysis, patient safety, economics research in nursing, mixed methods, global research dissemination Written by a distinguished group of international nursing scientists, scientists from related fields, and methodologists, the Handbook is the ideal reference for everyone involved in nursing science, whether they are graduate students, academics, editors and reviewers, or clinical investigators.