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Handbook of missing data methodology / [edited by] Geert Molenberghs...[et al.].

Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC handbooks of modern statistical methodsPublication details: Boca Raton : CRC press, c2015.Description: xxiv, 574 p. : illustrations ; 27 cmISBN:
  • 9781439854617 (hardcover : alk. paper)
Subject(s): DDC classification:
  • 000SA.01 23 M718
Contents:
1. Introduction and preliminaries / Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis -- 2. Developments of methods and critique of ad hoc methods / James R. Carpenter and Michael G. Kenward -- 3. Introduction and overview / Michael G. Kenward, Geert Molenberghs, and Geert Verbeke -- 4. Perspective and historical overview / Michael G. Kenward and Geert Molenberghs -- 5. Bayesian methods / Michael J. Daniels and Joseph W. Hogan -- 6. Joint modeling of longitudinal and time-to-event data / Dimitris Rizopoulos -- 7. Introduction and overview / Garrett M. Fitzmaurice -- 8. Missing data methods: a semi-parametric perspective / Anastasios A. Tsiatis and Marie Davidian -- 9. Double-robust methods / Andrea Rotnitzky and Stijn Vansteelandt -- 10. Pseudo-likelihood methods for incomplete data / Geert Molenberghs and Michael G. Kenward -- 11. Introduction / Michael G. Kenward -- 12. Multiple imputation: perspective and historical overview / John B. Carlin -- 13. Fully conditional specification / Stef van Buuren -- 14. Multilevel multiple imputation / Harvey Goldstein and James R. Carpenter -- 15. Introduction and overview / Geert Molenberghs, Geert Verbeke, and Michael G. Kenward -- 16. A likelihood-based perspective / Geert Verbeke, Geert Molenberghs, and Michael G. Kenward -- 17. A semi-parametric perspective / Stijn Vansteelandt -- 18. Bayesian sensitivity analysis / Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu -- 19. Sensitivity analysis with multiple imputation / James R. Carpenter and Michael G. Kenward -- 20. The elicitation and use of expert opinion / Ian R. White -- 21. Introduction and overview / Geert Molenberghs -- 22. Missing data in clinical trials / Craig Mallinckrodt -- 23. Missing data in sample surveys / Thomas R. Belin and Juwon Song -- 24. Model diagnostics / Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke-- Index.
Summary: It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.
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Includes bibliographical references and index.

1. Introduction and preliminaries / Garrett M. Fitzmaurice, Michael G. Kenward, Geert Molenberghs, Geert Verbeke, and Anastasios A. Tsiatis --
2. Developments of methods and critique of ad hoc methods / James R. Carpenter and Michael G. Kenward --
3. Introduction and overview / Michael G. Kenward, Geert Molenberghs, and Geert Verbeke --
4. Perspective and historical overview / Michael G. Kenward and Geert Molenberghs --
5. Bayesian methods / Michael J. Daniels and Joseph W. Hogan --
6. Joint modeling of longitudinal and time-to-event data / Dimitris Rizopoulos --
7. Introduction and overview / Garrett M. Fitzmaurice --
8. Missing data methods: a semi-parametric perspective / Anastasios A. Tsiatis and Marie Davidian --
9. Double-robust methods / Andrea Rotnitzky and Stijn Vansteelandt --
10. Pseudo-likelihood methods for incomplete data / Geert Molenberghs and Michael G. Kenward --
11. Introduction / Michael G. Kenward --
12. Multiple imputation: perspective and historical overview / John B. Carlin --
13. Fully conditional specification / Stef van Buuren --
14. Multilevel multiple imputation / Harvey Goldstein and James R. Carpenter --
15. Introduction and overview / Geert Molenberghs, Geert Verbeke, and Michael G. Kenward --
16. A likelihood-based perspective / Geert Verbeke, Geert Molenberghs, and Michael G. Kenward --
17. A semi-parametric perspective / Stijn Vansteelandt --
18. Bayesian sensitivity analysis / Joseph W. Hogan, Michael J. Daniels, and Liangyuan Hu --
19. Sensitivity analysis with multiple imputation / James R. Carpenter and Michael G. Kenward --
20. The elicitation and use of expert opinion / Ian R. White --
21. Introduction and overview / Geert Molenberghs --
22. Missing data in clinical trials / Craig Mallinckrodt --
23. Missing data in sample surveys / Thomas R. Belin and Juwon Song --
24. Model diagnostics / Dimitris Rizopoulos, Geert Molenberghs, and Geert Verbeke--
Index.

It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.
The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters.
Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

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