Svend Juul’s An Introduction to Stata for Health Researchers, Second Edition is distinguished in its careful attention to detail. The reader will learn not only how to use Stata but also the skills needed to create the reproducible analyses so necessary in the field.

The book is based on the assumption that the reader has some basic knowledge of statistics but no knowledge of Stata. Juul builds the reader’s abilities as a builder would build a house, laying a firm foundation in Stata, framing a general structure in which good work can be accomplished, and finally filling in details that are particular to various types of statistical analysis.

Juul starts by teaching the reader how to communicate with Stata, not just through its unified syntax, but also by demonstrating how Stata thinks about its basic building blocks. Juul shows how Stata views data and graphics, allowing the reader to see the variety of possible data structures. He also shows how to manipulate the data to create a dataset that is well documented and how to create carefully crafted graphs. He makes the book easy to use as a learning tool and easy to refer back to for useful techniques.

Once he introduces Stata to the new user, Juul fills in the details for performing analysis in Stata. As would be expected from a book addressing health researchers, Juul demonstrates mostly the statistical techniques common in biostatistics and epidemiology: case–control and matched case–control data analysis, stratified or not; linear and generalized linear models, including logistic, Poisson, and binomial regression; survival analysis with both life tables and proportional hazards; and classification using receiver operating characteristic curves.

While teaching Stata implementation, Juul reinforces habits that allow reproducible research and graceful backtracking in case of errors. Early in the book, he introduces how to use do-files for creating and log files for tracking work. At the end of the book, Juul introduces some useful programming techniques, such as loops and branching, that simplify repetitive tasks.

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