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Book Review: Regression methods in biostatistics: linear, logistic, survival, and repeated measures models

Given the title and particularly the sub-title, you might expect this book to be a monster of at least 500 pages liberally scattered with matrix algebra and estimation theory. But in truth, the book has only 340 pages and there is scarcely a matrix to be seen and only a minimal amount of technical material. Because of this, the book does largely provide what the authors claim, namely that their book gives ‘an accessible introduction to multipredictor models’, particularly for those readers with a good first course in statistics. The book begins with a brief account of what multipredictor regression modelling is all about, in the context of a data set concerned with the treatment of back pain. This short introduction is followed in Chapter 2 by a discussion of exploratory and descriptive techniques; each of the topics included in this chapter, data checking, types of data, graphical description, transformation of data and so on, gets little more than a page allocated to them, so the material is short in detail. Nevertheless, this chapter is useful as an aide-memoir for later chapters. In a similar vein, Chapter 3 provides a concise account of basic statistical methods such as t-test and analysis of variance. The writing is clear and readers will again find the chapter a useful reminder of topics they covered in their introductory statistics courses. In Chapter 4, Linear Regression, we begin to get to the core of the book. The multiple linear regression model is well described, in part, by using interesting data sets. Analyses are illustrated by output from the Stata statistics package, although no Stata code is given. Good accounts are given of how to handle multilevel categorical predictors, confounding, mediation and techniques for checking model assumptions and fit. Throughout this chapter, as in most of the book, the advice given is sensible and clear. Chapter 5 confronts the thorny problem of predictor selection and contains a lot of interesting and useful material about bias-variance trade-off, prediction error, collinearity and much more. The authors rightly emphasize that caution is needed when trying to identify a more parsimonious model and also demonstrate the potential pitfalls awaiting the unwary. Chapter 6 gives an excellent account of logistic regression, which again is helped by the use of absorbing examples, and Chapter 7 does the same for the Cox proportional hazard model. Both chapters highlight the need to check assumptions when fitting either type of models and describe a number of appropriate diagnostic tools. Chapter 8 is a useful summary of several approaches to the analysis of repeated measures and longitudinal data. In the chapter’s 40 pages, the authors describe response feature analysis, random effect models and generalized estimating equations; necessarily, each topic is covered relatively superficially but the authors still manage a good discussion of the differences between marginal and conditional models and some helpful comments on handling missing data. One doubt I have about this chapter relates to the comments made about the use of difference scores or analysis of covariance when dealing with, say, pre–post studies. Here I think the authors may have gone a little astray with their advice. Chapter 9 gives a short account of generalized linear models, and Chapter 10 gives an equally short account of complex surveys. Perhaps, these two chapters really are too concise to be of much use. These two chapters are followed by a short summary chapter in which the authors manage to give a lot of useful pointers about a variety of topics including consulting a statistician, which we all know is the most important advice of all. This book covers a lot of material without making technical demands on the reader. The exercises at the end of each chapter will greatly assist students (and others) who attempt them, in gaining an understanding of all the models described. Overall, Regression Methods in Biostatistics is an excellent book that does a very good job of describing some important statistical methodology for researchers