Svend Juul and Morten Frydenberg’s An Introduction to Stata for Health
Researchers, Third Edition is distinguished in its careful attention to
detail. The reader will learn not only how to use Stata for statistical
analysis but also the skills needed to make the analysis reproducible. The
authors use a friendly, down-to-earth tone and include tips gained from a
lifetime of collaboration and consulting.
The book is based on the assumption that the reader has some basic knowledge
of statistics but no knowledge of Stata. The authors build 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;
adding the details that are particular to various types of statistical
analyses; and finally, trimming with a thorough treatment of graphics.
Juul and Frydenberg start 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. The authors show how Stata
views data, thus allowing the reader to see the variety of possible data
structures. They also show how to manipulate data to create a dataset
that is well documented. When demonstrating analysis techniques, the authors
show how to think of analysis in terms of estimation and postestimation.
They make the book easy to use as a learning tool and easy to refer back to
for useful techniques.
Once they introduce Stata to new users, Juul and Frydenberg fill in the
details for performing analysis in Stata. As would be expected from a book
addressing health researchers, Juul and Frydenberg mostly demonstrate the
statistical techniques that are common in biostatistics and epidemiology:
case–control, matched case–control, and incidence-rate data analysis,
which can be stratified or not; linear and generalized linear models,
including logistic, Poisson, and binomial regression; survival analysis
with proportional hazards; and classification using receiver operating
characteristic curves. While presenting general estimation techniques, the
authors also spend time with interactions and techniques for checking model
While teaching Stata implementation, Juul and Frydenberg reinforce habits
that allow reproducible research and graceful backtracking in case of
errors. Early in the book, they introduce how to use do-files for creating
sequences and log files for tracking work. At the end of the book, they
introduce some useful programming techniques, such as loops and branching,
that simplify repetitive tasks.
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