Analyzing Longitudinal Clinical Trial Data

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Analyzing Longitudinal Clinical Trial Data


Analyzing Longitudinal Clinical Trial Data
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Analyzing Longitudinal Clinical Trial Data


Analyzing Longitudinal Clinical Trial Data
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Author : Craig Mallinckrodt
language : en
Publisher: CRC Press
Release Date : 2016-12-12

Analyzing Longitudinal Clinical Trial Data written by Craig Mallinckrodt and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Mathematics categories.


Analyzing Longitudinal Clinical Trial Data: A Practical Guide provide practical and easy to implement approaches for bringing the latest theory on analysis of longitudinal clinical trial data into routine practice.?This book, with its example-oriented approach that includes numerous SAS and R code fragments, is an essential resource for statisticians and graduate students specializing in medical research. The authors provide clear descriptions of the relevant statistical theory and illustrate practical considerations for modeling longitudinal data. Topics covered include choice of endpoint and statistical test; modeling means and the correlations between repeated measurements; accounting for covariates; modeling categorical data; model verification; methods for incomplete (missing) data that includes the latest developments in sensitivity analyses, along with approaches for and issues in choosing estimands; and means for preventing missing data. Each chapter stands alone in its coverage of a topic. The concluding chapters provide detailed advice on how to integrate these independent topics into an over-arching study development process and statistical analysis plan.

Longitudinal Data Analysis


Longitudinal Data Analysis
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Author : Garrett Fitzmaurice
language : en
Publisher: CRC Press
Release Date : 2008-08-11

Longitudinal Data Analysis written by Garrett Fitzmaurice and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-11 with Mathematics categories.


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 and applications. It also focuses on the assorted challenges that arise in analyzing longitudinal data. After discussing historical aspects, leading researchers explore four broad themes: parametric modeling, nonparametric and semiparametric methods, joint models, and incomplete data. Each of these sections begins with an introductory chapter that provides useful background material and a broad outline to set the stage for subsequent chapters. Rather than focus on a narrowly defined topic, chapters integrate important research discussions from the statistical literature. They seamlessly blend theory with applications and include examples and case studies from various disciplines. Destined to become a landmark publication in the field, this carefully edited collection emphasizes statistical models and methods likely to endure in the future. Whether involved in the development of statistical methodology or the analysis of longitudinal data, readers will gain new perspectives on the field.

Analysis Of Longitudinal Data


Analysis Of Longitudinal Data
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Author : Peter Diggle
language : en
Publisher: Oxford University Press
Release Date : 2013-03-14

Analysis Of Longitudinal Data written by Peter Diggle and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Language Arts & Disciplines categories.


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.

Methods And Applications Of Longitudinal Data Analysis


Methods And Applications Of Longitudinal Data Analysis
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Author : Xian Liu
language : en
Publisher: Elsevier
Release Date : 2015-09-11

Methods And Applications Of Longitudinal Data Analysis written by Xian Liu and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-11 with Mathematics categories.


Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

Topics In Analyzing Longitudinal Data


Topics In Analyzing Longitudinal Data
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Author : Hyunsu Ju
language : en
Publisher:
Release Date : 2005

Topics In Analyzing Longitudinal Data written by Hyunsu Ju and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.


We propose methods for analyzing longitudinal data, obtained in clinical trials and other applications with repeated measures of responses taken over time. Common characteristics of longitudinal studies are correlated responses and observations taken at unequal points in time. The first part of this dissertation examines the justification of a block bootstrap procedure for the repeated measurement designs, which takes into account the dependence structure of the data by resampling blocks of adjacent observations rather than individual data points. In the case of dependent stationary data, under regular conditions, the approximately studentized or standardized block bootstrap possesses a higher order of accuracy. With longitudinal data, the second part of this dissertation shows that the diagonal optimal weights for unbalanced designs can be made to improve the efficiency of the estimators in terms of mean squared error criterion. Simulation study is conducted for each of the longitudinal designs. We will also analyze repeated measurement data set concerning nursing home residents with multiple sclerosis, which is obtained from a large database termed the minimum data set (MDS).

Missing Data In Longitudinal Studies


Missing Data In Longitudinal Studies
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Author : Michael J. Daniels
language : en
Publisher: CRC Press
Release Date : 2008-03-11

Missing Data In Longitudinal Studies written by Michael J. Daniels and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-11 with Mathematics categories.


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 several data sets throughout that cover a range of study designs, variable types, and missing data issues. The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness. With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

Preventing And Treating Missing Data In Longitudinal Clinical Trials


Preventing And Treating Missing Data In Longitudinal Clinical Trials
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Author : Craig H. Mallinckrodt
language : en
Publisher: Cambridge University Press
Release Date : 2013-01-28

Preventing And Treating Missing Data In Longitudinal Clinical Trials written by Craig H. Mallinckrodt and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-28 with Medical categories.


Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. This book focuses on the prevention and treatment of missing data in longitudinal clinical trials. Based on his extensive experience with missing data, the author offers advice on choosing analysis methods and on ways to prevent missing data through appropriate trial design and conduct. He offers a practical guide to key principles and explains analytic methods for the non-statistician using limited statistical notation and jargon. The book's goal is to present a comprehensive strategy for preventing and treating missing data, and to make available the programs used to conduct the analyses of the example dataset.