Regression Methods in Biostatistics (2nd Ed., 2nd ed. 2012)
Linear, Logistic, Survival, and Repeated Measures Models
Statistics for Biology and Health Series

Language: English

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Regression Methods in Biostatistics (2nd Ed.)
Publication date:
509 p. · 15.5x23.5 cm · Paperback

116.04 €

In Print (Delivery period: 15 days).

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Regression methods in biostatistics
Publication date:
509 p. · 15.5x23.5 cm · Paperback

This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes.

Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.

The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided.

Introduction.- Exploratory and Descriptive Methods.- Basic Statistical Methods.- Linear Regression.- Logistic Regression.- Survival Analysis.- Repeated Measures Analysis.- Generalized Linear Models.- Strengthening Casual Inference.- Predictor Selection.- Complex Surveys.- Summary.

The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
Short and to the point so that the important issues and similarities between the methods, rather than the differences, shine through Provides a unified introduction to the regression methods listed in the title Unified approach should appeal to students who learn conceptually and verbally Includes supplementary material: sn.pub/extras