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This is the preliminary program for the 2009 Joint Statistical
Meetings in Washington, DC.
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The views expressed here are those of the individual authors and not necessarily those of the ASA or its board, officers, or staff. Back to main JSM 2009 Program page |
= Applied Session,
= Theme Session,
= Presenter| CE_02C | Sat, 8/1/09, 8:30 AM - 5:00 PM | CC-204C |
| Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis - Continuing Education - Course | ||
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ASA, Biometrics Section |
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| Instructor(s): Michael J. Daniels, University of Florida, Joseph W. Hogan, Brown University | ||
| This course provides a survey of modern model-based approaches to handling dropout in longitudinal studies and illustrates the use of newly developed methods for sensitivity analysis and incorporation of prior information. Emphasis is on Bayesian approaches, but the models and methods discussed can be implemented in non-Bayesian settings. The course is divided into three parts: Part 1 includes a brief review of models for longitudinal data and the basics of Bayesian inference; Part 2 focuses on formal classification of dropout and missing data mechanisms, classes of models that can be used to adjust for biases caused by dropout, and the logistics of model fitting; Part 3 deals with specification and fitting of models to handle nonignorable (informative) dropout, with emphasis on the role of sensitivity analysis and informative prior distributions for encoding key assumptions. We look at three case studies that illustrate many of the concepts introduced during the course and build on each case study to illustrate progressively more complex analyses. The case studies include real-time demonstration of model fitting using WinBUGS software and the R-to-WinBUGS interface. | ||
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JSM 2009
For information, contact jsm@amstat.org
or phone (888) 231-3473. If you have questions about the Continuing Education program,
please contact the Education Department. |