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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models, Second Edition

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Authors:
Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, and Charles E. McCulloch
Publisher: Springer
Copyright: 2012
ISBN-13: 978-1-4614-1352-3
Pages: 509; hardcover
Price: $68.50

Comment from the Stata technical group

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models, Second Edition is intended as a teaching text for a one-semester or two-quarter secondary statistics course in biostatistics. The book's focus is multipredictor regression models in modern medical research. The authors recommend as a prerequisite an introductory course in statistics or biostatistics, but the first three chapters provide sufficient review material to make this requirement not critical.

Vittinghoff, Glidden, Shiboski, and McCulloch take a unified approach to regression models. They begin with linear regression and then discuss issues such as model statement and assumptions, types of regressors (for example, categorical versus continuous), interactions, causation and confounding, inference and testing, diagnostics, and alternative models for when assumptions are violated. Then they discuss these same issues in the contexts of other multipredictor regression models, namely, logistic regression, the Cox model, and generalized linear models (GLMs). The authors then cover generalized estimating equations (GEE) and the analysis of survey data. Almost all analyses are performed using Stata.

The second edition provides two new chapters and substantially expands some of the existing chapters. Specifically, a new chapter on strengthening causal inference describes the fundamentals of causal inference and concentrates on two estimation methods—inverse probability weighting and what the authors call potential outcomes estimation. This chapter also covers propensity scores, time-dependent treatments, instrumental variables, and principal stratification. The other new chapter is on missing data. The authors describe the missing-data problem and its impact on statistical inference. They then discuss three approaches for handling missing data: maximum likelihood estimation, multiple imputation, and inverse weighting. Among the substantially revised chapters are chapters on logistic regression, now including categorical outcomes; on survival analysis, now including competing risks; on generalized linear models, now including negative binomial and zero-truncated and zero-inflated count models; and more. All the Stata examples used in the book have been updated for Stata 12.

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