Published November, 2010
By Luc Duchateau and Paul Janssen
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Statistics for Biology and Health
Rating:
Availability: Available

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data.

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Published October, 2010
By Peter Schlattmann
Publisher: Springer (Springer-Verlag Berlin and Heidelberg GmbH & Co. K)
Series: Statistics for Biology and Health
Rating:
Availability: Available

Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the "average" patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model.

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Published July, 2010
By David G. Kleinbaum and Mitchel Klein
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Statistics for Biology and Health
Rating:
Availability: Available

The three new chapters are as follows: Chapter 8: Additional Modeling Strategy Issues Chapter 9: Assessing Goodness of Fit for Logistic Regression Chapter 10: Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves In adding these three chapters, we have moved Chaps.

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Published February, 2010
By Adrian Barnett, Annette J. Dobson and Adrian G. Barnett
Publisher: Springer (Springer-Verlag Berlin and Heidelberg GmbH & Co. K)
Series: Statistics for Biology and Health
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Availability: Available

With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely.This is the first book on statistical methods for seasonal data written for a health audience.

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Published February, 2010
By Toshiro Tango
Publisher: Springer (Springer New York)
Series: Statistics for Biology and Health
Rating:
Availability: Not available (reason unspecified)

This book offers a modern perspective on statistical methods for detecting disease clustering. It offers analysis and illustration of methods for a variety of real data sets, and will provide an invaluable resource for a wide ranging of audience.

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Published December, 2009
By Alexander Eric Kramer, Mirjam Kretzschmar, Klaus Krickeberg and Alexander Krämer
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Statistics for Biology and Health
Rating:
Availability: Available

The book introduces the reader to methodological aspects of epidemiology that are specific for infectious diseases and provides insight into the epidemiology of some classes of infectious diseases characterized by their main modes of transmission.

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Published November, 2009
By Claudio Jose Struchiner, M. Elizabeth Halloran, Ira M. Longini, Ira M. Longini, Jr. and Claudio J. Struchiner
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Statistics for Biology and Health
Rating:
Availability: Available

As well as being a reference for the design, analysis, and interpretation of vaccine studies, the text covers all design and analysis stages, from vaccine development to post-licensure surveillance, presenting likelihood, frequentists, and Bayesian approaches.

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Published July, 2009
By Karl W. Broman, Saunak Sen, Karl Broman and Sen Saunak
Publisher: Springer (Springer New York)
Series: Statistics for Biology and Health
Rating:
Availability: Not available (reason unspecified)

Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. This illustrated book is a comprehensive guide to the practice of QTL mapping and the use of R/qtl.

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Published May, 2009
By Timothy L. Lash, Matthew P. Fox and Aliza K. Fink
Publisher: Springer (Springer-Verlag New York Inc.)
Series: Statistics for Biology and Health
Rating:
Availability: Available

Bias analysis quantifies the influence of systematic error on an epidemiology study's estimate of association. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely.

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Published March, 2009
By Peter Schlattmann
Publisher: Springer (Springer-Verlag Berlin and Heidelberg GmbH & Co. K)
Series: Statistics for Biology and Health
Rating:
Availability: Available

Patients are not alike! This simple truth is often ignored in the analysis of me- cal data, since most of the time results are presented for the "average" patient. As a result, potential variability between patients is ignored when presenting, e.g., the results of a multiple linear regression model.

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