Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Monographs on Statistics and Applied Probability) | 
 enlarge | Authors: Michael J. Daniels, Joseph W. Hogan
Publisher: Chapman & Hall/CRC
Category: Book
List Price: $79.95
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Avg. Customer Rating:  1 reviews
Sales Rank: 752224
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 328
Shipping Weight (lbs): 1.3
Dimensions (in): 9.3 x 6.1 x 0.9
ISBN: 1584886099
Dewey Decimal Number: 519.5
EAN: 9781584886099
ASIN: 1584886099
Publication Date: March 11, 2008
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Product Description
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.
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great new book on the subject, good at theory and practice May 10, 2008
11 out of 15 found this review helpful
Issues of missing data in longitudinal studies are very important in the design and analysis of clinical trials. This is such an important statistical topic, that many excellent books have been written about it. One of the earliest and a landmark text was the book by Rubin and Little which was recently updated in the second edition. Mixed linear models for longitudinal data provide an effective method for dealing with several types of missingness as does multiple imputation. Pattern mixture models are also very useful. Molenberghs and Kennard, Verbeke and Molenberghs and Rubin all cover these topic well in their excellent texts.
What then is the advantage of this text by Daniels and Hogan?
1. It is slightly more current than the others
2. It combines theory and application very nicely
3. A series of seven real data sets from real clinical trials and epidemiologic studies are presented up front in Chapter 1 and used throughout to illustrate practical advantages and disadvantages of the various techniques covered in the latter chapters
4. It covers Bayesian modeling and sensitivity analysis in more depth that most of its competitors
Only Molenberghs and Kennard match it in the depth of coverage on theory and applications. But they do not provide the coverage of Bayesian methods the way Daniels and Hogan do.
For these reasons I recommend this book to the practicing biostatisticians working on clinical trials even if the texts listed below are alresdy on their bookshelves.
I) Diggle, P. J., Heagerty, P., Liang, K.-Y. and Zeger, S. L. (2002). "Analysis of Longitudinal Data" 2nd Edition. Oxfrod University Press, Oxford.
II) Fitzmaurice, G. M., Laird, N.M. and Ware, J. H. (2004). "Applied ongitudinal Analysis". John Wiley & Sons, New York.
III) Little, R. J. A. and Rubin, D. B. (2002) "Statistical Analysis with Missing Data" 2nd Edition, John Wiley & Sons, New York
IV) Molenberghs, G and Kennard, M. G. (2007). "Missing Data in Clinical Studies" John Wiley & Sons, Chichester.
V) Molenberghs, G. and Verbeke, G. (2005). "Models for Discrete Longitudinal Data". Springer-Verlag, New York.
VI) Pinheiro, J. C. and Bates, D. M. (2000). "Mixed Effects Models in S and S-Plus". Springer-Verlag, New York.
VII) Rubin, D. B. (1987). "Multiple Imputation for Nonresponse in Surveys" John Wiley & Sons, New York.
VIII) Tsiatis, A. A. (2006). "Semiparametric Theory and Missing Data" Dpringer-Verlag, New York.
IX) Verbeke, G and Molenberghs, G. (1997). "Linear Mixed Models in Practice: A SAS-Oriented Approach" Springer-Verlag, New York.
X) Verbeke, G and Molenberghs, G. (2000). "Linear Mixed Models for Longitudinal Data" Springer-Verlag, New York.
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