Last edited by Basar
Wednesday, May 13, 2020 | History

10 edition of Missing Data in Longitudinal Studies found in the catalog.

Missing Data in Longitudinal Studies

Strategies for Bayesian Modeling and Sensitivity Analysis (Monographs on Statistics and Applied Probability)

by Michael J. Daniels

  • 215 Want to read
  • 27 Currently reading

Published by Chapman & Hall/CRC .
Written in English

    Subjects:
  • Probability & statistics,
  • Mathematics,
  • Science/Mathematics,
  • Mathematics / Statistics,
  • Biostatistics,
  • Probability & Statistics - General,
  • Longitudinal method,
  • Missing observations (Statistics),
  • Sensitivity theory (Mathematics)

  • The Physical Object
    FormatHardcover
    Number of Pages352
    ID Numbers
    Open LibraryOL12313753M
    ISBN 101584886099
    ISBN 109781584886099

    Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. handle missing data is a very important problem in longitudinal studies. According to huge papers, it is clear that no universal method can be considered definitive for any missingness scenarios. Instead, different methods need to rely on the different patterns and types of missing data, the latter one is also known as missing data mechanisms.

    Introduction to Longitudinal Data 2 1. Some examples and questions of interest Longitudinal studies: Studies where a response is observed on each subject/unit repeatedly over time are commonplace, e.g., † Clinical trials, observational studies in humans, animals † Studies of growth and decay in agriculture, chemistry Key messages in this talk. Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives.

    Cambridge U nive rsit y Pre ss - Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide Craig H. MallinckrodtFile Size: KB. LONGITUDINAL AND MULTIGROUP MODELING WITH MISSING DATA 1 Werner Wothke, SmallWaters Corp. Longitudinal and multi-group modeling with missing data Reprinted with permission from T.D. Little, K.U. Schnabel and J. Baumert [Eds.] () Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples.


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Missing Data in Longitudinal Studies by Michael J. Daniels Download PDF EPUB FB2

The preliminary sections on longitudinal data analysis, Bayesian statistics, and missing data are well written and serve to make this book a self-contained reference. The models presented to analyze missing data in longitudinal studies cover many ideas from the current literature, and some of the methods are at the cutting edge of research.

Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area.

This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic by: The preliminary sections on longitudinal data analysis, Bayesian statistics, and missing data are well written and serve to make this book a self-contained reference.

The models presented to analyze missing data in longitudinal studies cover many ideas from the current literature, and some of the methods are at the cutting edge of : $ Missing Data in Longitudinal Studies 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.

Summary 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. Missing Data in Longitudinal Studies Hedeker D & Gibbons RD (). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, Chapter 14 in Hedeker & Gibbons (), Longitudinal Data Analysis, Wiley.

1File Size: 1MB. J Clin Epidemiol. Apr;55(4) Attrition in longitudinal studies. How to deal with missing data. Twisk J(1), de Vente W.

Author information: (1)Institute for Research in Extramural Medicine, Vrije Universiteit, Vd Boechorststraat 7, BT, Amsterdam, The Netherlands. @ The purpose of this paper was to illustrate the influence of missing data on the results Cited by:   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. Application of Random-Effects Pattern-Mixture Models for Missing Data in Longitudinal Studies Donald Hedeker and Robert D.

Gibbons University of Illinois at Chicago Random-effects regression models have become increasingly popular for analysis of longitudinal Size: KB.

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 employCited by:   The models presented to analyze missing data in longitudinal studies cover many ideas from the current literature, and some of the methods are at the cutting edge of research.

The book will probably have greatest appeal to statisticians with a research interest in missing data.4/5(1). 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.

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.

There is an enormous literature on literature missing data methods in longitudinal studies. We refer the reader to the excellent books by Diggle et al. (), Fitzmaurice et al. Missing Data in Longitudinal Studies. DOI link for Missing Data in Longitudinal Studies.

Missing Data in Longitudinal Studies book. Strategies for Bayesian Modeling and Sensitivity Analysis. By Michael J. Daniels, Joseph W. Hogan. Edition 1st Edition. First Published eBook Published 11 March Pub. location New York. Imprint Author: Michael J. Daniels, Joseph W. Hogan. Get this from a library.

Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis. [M J Daniels; Joseph W Hogan] -- "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.

The second edition includes new chapters on the role of the time variable and presents new features of longitudinal data analysis. Explanations have been clarified where necessary and several chapters have been completely rewritten.

The analysis of data from experimental studies and the problem of missing data in longitudinal studies are discussed. Xian Liu, in Methods and Applications of Longitudinal Data Analysis, Missing data. In longitudinal data analysis, missing observations are almost always present.

In biomedical studies, subjects may drop out of follow-ups due to health-related reasons. In observational research, survey respondents at baseline are lost to subsequent investigation because of death, out-of-scope. Missing completely at random. Values in a data set are missing completely at random (MCAR) if the events that lead to any particular data-item being missing are independent both of observable variables and of unobservable parameters of interest, and occur entirely at random.

When data are MCAR, the analysis performed on the data is unbiased; however, data are rarely MCAR. 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 by:. Summary This chapter contains sections titled: Introduction Missing Data Mechanisms Models and Missing Data Mechanisms Testing MCAR Models for Nonignorable Missingness Summary Missing Data in Longitudinal Studies - - Wiley Series in Probability and Statistics - .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.Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis.

[Michael Joseph Daniels; Joseph W Hogan] -- Focuses on how to handle missing data in longitudinal studies, offering coverage of models for longitudinal data, missing data mechanisms, and various approaches to sensitivity analysis. This book.