Missing Data in Clinical Studies 1st Edition

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ISBN-13: 978-0470849811
ISBN-10: 0470849819
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Frequently Bought Together

Missing Data in Clinical Studies + Handbook of Missing Data Methodology (Chapman & Hall/CRC Handbooks of Modern Statistical Methods) + Clinical Trials with Missing Data: A Guide for Practitioners (Statistics in Practice)
Price for all three: $256.39

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Editorial Reviews

Review

?Overall, this is an excellent text on missing data that is engaging for practitioners while being rigorous enoughfor use in the graduate biostatistics courses.?(Biometrics , September 2009)" "Missing Data in Clinical Studies does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies." (Journal of the American Statistician, December 2008)

"?this book is reasonably well organized and covers all the relevant theory and much of the practical applications of the field." (Journal of the American Chemical Association, August 6, 2008)

"Missing Data in Clinical Studies does an excellent job of presenting essential ideas on modern concepts and techniques relevant to missing data in clinical studies."  (Journal of the American Statistician, December 2008)

"Clear, generally accessible and well written, and the content is rich.  This text is a highly recommendable addition to the shelves of practicing statisticians." (Journal of Applied Statistics, August 2008)

"The authors give key examples in the form of several clinical trials and their analyses using the appropriate remedial techniques."  (Journal of Tropical Pediatrics, August 2007)

From the Back Cover

The detrimental effects of incomplete data sets on the results of clinical trials are both well known and all too commonly recurrent. It is essential that the correct statistical methodology be applied in order to effectively analyse the results of trials affected by missing data.

Missing Data in Clinical Trials provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described.

  •  Provides a practical guide to the analysis of clinical trials and related studies with missing data.
  •  Examines the problems caused by missing data, enabling a complete understanding of how to overcome them.
  •  Presents conventional, simple methods to tackle these problems, before addressing more advanced approaches, including sensitivity analysis, and the MAR missingness mechanism.
  •  Illustrated throughout with real-life case studies and worked examples from clinical trials.
  •  Details the use and implementation of the necessary statistical software, primarily SAS.

Missing Data in Clinical Trials has been developed through a series of courses and lectures. Its practical approach will appeal to applied statisticians and biomedical researchers, in particular those in the biopharmaceutical industry, medical and public health organisations. Graduate students of biostatistics will also find much of benefit.

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Product Details

  • Hardcover: 526 pages
  • Publisher: Wiley; 1 edition (April 16, 2007)
  • Language: English
  • ISBN-10: 0470849819
  • ISBN-13: 978-0470849811
  • Product Dimensions: 6.3 x 1.3 x 9.2 inches
  • Shipping Weight: 1.9 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #669,840 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

47 of 47 people found the following review helpful By Michael R. Chernick on May 10, 2008
Format: Hardcover
This is a very good guide to missing data which plays such a major role in clinical studies. Daniels and Hogan's book is a competitor. They are both current, cover the important methods, models and estimation algorithms. The Hogan book is a little more recent. This text is a little more detailed and has a larger bibliography. Both books are very practical. They both use case studies well to illustrate the value of the methods and to compare them. Daniels and Hogan spend a lot of time with Bayesian approaches and MCMC methods While these authors stick to describing the classical methods in great detail.

I recommend this highly for biostatisticians just as I did for the Daniels and Hogan text.
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