I've been very gratified by the success of the first edition of this book. Although the positive reviews from biostatisticians have meant a lot to me, the real payoff has been the response from novice clinical investigators. Comments such as “easy to read,” “easy to understand,” and “helpful and useful” have greatly warmed my heart. In one case, the book even led me to collaborate with a reader (entirely by email) on a project of his. This is exactly why I wrote the book: to promote the work of clinical researchers early in their careers.

Writing a second edition has enabled me to make some important additions to the book. Since the time I wrote the first edition, there has been a major increase in the use of generalized estimating equations and mixed-effects models to analyze correlated (clustered) observations. Such data arise from longitudinal studies that evaluate subjects repeatedly for a particular outcome. Clustered data also arise from other types of studies where patients are randomized or sampled from established groups such as physician practices or hospital. In addition to generalized estimating equations and mixed-effects models, I also explain how to use repeated measures analysis of variance, conditional logistic regression, and extensions of the Cox proportional hazard model to analyze clustered data (Chapter 12).