Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis

Isham, Valerie (Series edited by)/ Tibshirani, R.J. (Series edited by)/ Louis, Thomas A. (Series edited by)/ Tong, Howell (Series edited by)/ Keiding, Niels (Series edited by)/ Murphy, Susan.A (Series edited by)/ Daniels, Michael J. (Author)/ Hogan, Joseph W. (Author)

ISBN 10: 1584886099 / ISBN 13: 9781584886099

Published by Chapman & Hall, 2008

About this Item

1st edition. 416 pages. 9.50x6.25x1.00 inches. In Stock.

Seller Inventory # x-1584886099

Bibliographic Details

• Title: Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis
• Publisher: Chapman & Hall
• Publication Date: 2008
• Language: English
• ISBN 10: 1584886099
• ISBN 13: 9781584886099
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.64 kilograms

About this title

Synopsis

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.

About the Author

Michael J. Daniels, Joseph W. Hogan