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

Michael J. Daniels, Joseph W. Hogan

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March 11, 2008 by Chapman and Hall/CRC
Professional - 328 Pages - 21 B/W Illustrations
ISBN 9781584886099 - CAT# C6099
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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Features

  • Contains a large number of examples and case studies, including trials and studies on schizophrenia, aging, HIV/AIDS, and smoking cessation
  • Applies the specification of priors, Markov chain Monte Carlo algorithms, model fit assessment, and other Bayesian inference methods to real data
  • Describes commonly used missing data mechanisms, such as missing at random (MAR), missing not at random (MNAR), and ignorability, and shows how they are used in longitudinal data
  • Presents a Bayesian framework for drawing principled inferences from incomplete data, including methods for sensitivity analysis and the incorporation of informative prior information
  • Makes recommendations for the use of standard regression, mixture, selection, and varying coefficient models in different settings
  • Implements many of the analyses using WinBUGS, offering the code on a supplementary web page
  • 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 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.