John McGready

English

Research Papers in Economics

The Stata Journal (2003)
3,N umber 1, pp. 100 104
Review of A Short Introduction to Stata For
Biostatistics by Hills and De Stavola
John McGready
Johns Hopkins University
jmcgread@jhsph.edu
Abstract. The new book by Hills and De Stavola (2002)i sr e viewed.
Keywords: gn0005, biostatistics
1I ntroduction
A Short Introduction to Stata for Biostatistics is a welcome addition to the growing
body of literature that teaches statistical computing via an example and context-driven
approach. While a reader needs to have both some statistical background and some
computer literacy to use this book, the text can simultaneously assist in solidifying
the ability to interpret the results of various statistical analyses while teaching how to
produce these results. Stata provides some of the best documentation I have seen for
statistical software, but this documentation is not very helpful for getting a user up
and running, and a book of this nature can save a lot of time and spare a lot of angst.
The authors state that the book is  written with biostatisticians in mind . While
not denying the utility of a book like this to biostatistical professionals, I would argue
that the resulting book is more appropriate for those with less grounding in statistical
sciences and computing. I will follow-up this claim with speci cs later in this review.
The text is written in an interactive fashion, in a style that I dub  call and response .
By this, I mean that the book should be read in front of the computer. The book comes
with a CD-ROM of datasets providing much fodder for examples. Each description or
explanation is reinforced by a set of corresponding command statements, which can be
performed by the reader. Hence, the entire text from beginning to end is an interactive
journey between text-based explanations (the call) and user participation with Stata
(the response). This is an excellent way to present the material, as it provides the user
with instant grati cation in regard to each concept and corresponding set of commands.
It also familiarizes the reader with how Stata  thinks , operates, and presents results.
The book begins with a short introduction to Stata, a description of the on-screen
layout, and the Stata-speci c menu bars. This section also provides the reader with
instructions on how to upload the datasets for the book, which are provided in an
accompanying CD-ROM.W hile this introductory section is very useful, it is rather bare-
bones basic. One of the most frequent diﬃculties I witness when teaching with Stata is
the lack of students  understanding about the concept of a working directory in Stata. If
this is not cleared up at the beginning of the learning process, it inevitably causes many
delays down the road: datasets  can t be found , log  les are  lost  on the hard drive,
c ￿ 2003 Stata Corporation gn0005John McGready 101
and so on. While it could be argued that directory structures and the corresponding
working directories concept should be covered in literature speci c to each operating
system, a short treatment of this at the beginning of the text would be a very welcome
addition. The book does ultimately detail the organization of Stata program-speci c
directories in the  nal chapter, Chapter 15, but it still does not explore this working
directory concept. This shortcoming is not speci c to this text. Another very well-
written book on Stata, Lawrence Hamilton s Statistics with Stata 7 (2002), gives this
no mention either.
2D a t a m anagement
The data management chapters sandwich the rest of the material discussed in the book.
Chapters 1 3 give a very nice, concise introduction to the most commonly needed data
management techniques and should be required reading for anyone planning to do data
analysis with Stata. More advanced topics related to data management are sprinkled
throughout Chapter 5 and covered in Chapters 12 15, the  nal three chapters in the
book.
Chapter 1 on ‘Some Basic Commands  gets things started, illustrating how to open
aS tata dataset and get a feel for what it contains. The describe, codebook, list,
summarize, format, tabulate, sort,a n dgenerate commands are explored in detail,
as well as command-speci c options. Also detailed in this chapter are how to use Stata
as a calculator, Stata command syntax diagrams, and Stata s help utilities.
Chapter 2 on ‘Housekeeping  answers the question  Now that I ve got my dataset
up and running on Stata, and know what it contains, how do I make it work for me? .
The chapter details the basics of labeling a dataset, variables and values of a variable,
recoding variables, how to deal with dates, and missing values in Stata. Also introduced
in this Chapter are log  les and do- les. While the chapter is brief, it is chock-full of
exercises and highlights two very important points that if overlooked can cause a data
analyst much grief:
1. Stata considers any numeric missing value (coded as .)t oa ssume an arbitrarily
large value and treats it as a large number in logical operations: hence, caution
is necessary when applying variable-speci c operations. If a user types list if
age > 10,t his command will list all data records in which age is larger than 10,
including those with a missing value.
2. The pros and cons of labeling variable values: sometimes labeling values of a
variable taking on a discrete number of levels (gender, race, education category,
etc.) can cause confusion if the variable is later recoded.
While the section on do- les is helpful, it fails to highlight a nice, built-in feature
in Stata, the ability to generate a do- le via the command review window, which can
save a lot of typing, cutting and pasting, and toggling back and forth between the main
Stata screen and the do- le editor.102 Review of Hills and De Stavola
Chapter 3 shatters the illusion that all existing data are ready for reading into Stata
but oﬀers plenty of helpful tips and exercises on importing data to Stata from other
sources (spreadsheets, word processors, tab-delimited  les). Chapter 13 deals with data
management issues related to repeated measures data and the concepts of ‘wide  and
‘long  data formats. Nice examples are given on switching between formats, graphing
repeated measures data, and merging  les in the long format. Chapter 14 introduces
ado- les and steps the reader through the construction of several ado- les designed to
do useful data management and graphics tasks. Chapter 15 is a critical chapter and
should also be required reading for any Stata user: here are the secrets of updating
Stata via the Internet and downloading user-written ado- les. Also given is the web
address for the Stata Journal, which of course will allow readers who subscribe to access
this review!
3G r a phics
After three chapters on the basics of data management, Chapter 4 tackles the basics of
graphing in Stata. Stata is a mixed blessing when it comes to graphics: while it is rather
simple to get graphical output with Stata, making this output aesthetically pleasing can
be frustrating. Furthermore, Stata s help menu entries on graphing are cumbersome
and confusing to read. Chapter 4 s treatment of Stata s graphical capabilities is quite
nice. It gives the basics of making histograms, boxplots, cumulative distributions, and
scatterplots. The chapter also details useful aesthetic enhancements: adding titles,
labels, and lines to graphs, and selecting various symbols to mark data points. Also
covered are saving, combining, and printing graphs.
The  rst 4 chapters constitute a perfect sequencing of the basics of commencing a
successful data analysis. After covering only 4 chapters, readers can get their data into
Stata, explore it, modify it for their needs, and then look at it!
4S upplied programs
The CD-ROM that comes with the book includes some downloadable, menu-driven pro-
grams. There is a program for constructing tables, tabmenu1,a n dap r ogram for per-
forming multiple regressions, effmenu1.C hapter 6 gives a detailed account of the uses
of tabmenu1.T his is a very useful command, which essentially duplicates the capabil-
ities of the table command in Stata. However, tabmenu1 is much easier to use than
table and includes more options. In addition to the display of variable means, stan-
dard deviations and frequencies, tabmenu1 also allows the user to display con dence
intervals, the odds of a binary outcome, and rates as computed as count per exposure
time. These quantities can be broken out by levels of up to two explanatory variables.
What a powerful tool for exploratory data analysis this feature is!
I wish I could say the same about the effmenu1 program. The name derives from the
word eﬀect, as the program is designed to estimate the ‘eﬀect  of an explanatory variable
on an outcome variable, allowing adjustment for other factors. In other words, this is aJohn McGready 103
menu-driven program designed to make multiple regression easy to do. While I do not
wish to split semantic hairs, I would rather the program be called something along the
lines of assocmenu in honor of the word association (unfortunately, the pre x does not
lend itself to further abbreviation), as eﬀect is a very strong word to describe statistical
relationships. When I mentioned earlier that this book may ultimately not be targeted
to biostatisticians, it is partly because of the reliance on effmenu1 in Chapters 7 and 8.
Chapters 7 and 8 give detailed examples of how to employ effmenu1 to perform mul-
tivariate linear, logistic, and Poisson models accounting for confounders and exploring
statistical interaction. No doubt effmenu1 is a very handy tool for producing estimates
of adjusted associations. However, the only information given in the output relates the
outcome variable to the primary exposure of interest: no information is given about the
coeﬃcient values or con dence intervals for the controlling variables. In many obser-
vational and case-control studies, the substantive focus is on the adjusted relationship
between an outcome and a primary exposure. The adjusted associations between the
outcome and each controlling variable are in comparison of little interest. However, a lot
can be learned about a dataset by monitoring all adjusted associations in a regression
model and how these associations change (or do not change) in the presence of other
controlling variables. This type of approach can not be employed via effmenu1,a si t
only reports the estimates related to the primary ‘eﬀect  variable speci ed.
Chapters 9 and 10 cover the same subject matter as Chapters 7 and 8 but employ
Stata regression commands (regress, logit, poisson)t oe stimate outcome-predictor
associations. These commands are more  exible than effmenu1,a nd Stata has some
really nice features that complement its suite of regression commands. The book does a
good job of detailing how to use xi when including categorical predictors in a regression
model and of highlighting the built-in prediction commands. Chapter 10 gives a very
concise, useful explanation of modeling and testing statistical interaction in regression.
However, many additional regression-related features and commands in Stata are omit-
ted in these two chapters. Many of Stata s features for assessing the validity of a linear
regression model are ignored. Similarly, there is no mention of robust regression meth-
ods, generalized estimating equations approaches to regression on correlated outcomes,
or bootstrapping methods. No mention is made of commands for performing ordinal
or multinomial regression. While the chapters do a great job of explaining and illus-
trating the material they cover, the breadth of topics would not satisfy a professional
biostatistician.
5S u r v i val analysis
Stata has a great collection of commands and functions for doing survival analysis.
Chapter 11 highlights and gives examples of many of these features. The chapter gives
adequate attention to creating a survival dataset, given a failure-time variable and a
censoring indicator, with stset.T h ec h a p ter covers constructing Kaplan Meier curves,
computing time-varying event rates, and both parametric and Cox regression models for
failure-time data. Interestingly enough, no mention is made of either the log-rank test104 Review of Hills and De Stavola
or the Gehan two-sample test, nor is there any discussion of incorporating time-varying
covariates into the Cox model. The latter information is missed because Stata is so
adept for this problem.
6S ummary
In general, this is an excellent introductory level book, which will allow its readers to
be functional with Stata within a short time period. I have mentioned what I consider
to be a few key omissions from the text. One other curious omission is reference to the
StataQuest menu system. This user-friendly, free download from Stata would be the
perfect accompaniment to the authors  included tabmenu1 and effmenu1 commands.
One of the nicest things about reviewing the book is how much I learned about
Stata s capabilities in the areas of data management. It s amazing how set in my ways I
had become, sometimes using ineﬃcient methods simply because that s all I knew, and
Iw asn t able to improve on them using Stata s documentation. I am especially grateful
to the authors for introducing me to the compress command for optimizing variable
storage, and the egen, cut() function for categorizing continuous measures. The in-
teractive nature of the book enriches the learning experience and makes the information
real in the hands of the reader. Although it would not stand by itself as an appropri-
ate text or reference for individuals interested in advanced biostatistical applications
in Stata, this book can serve as an excellent accompanying text for introductory epi-
demiology and biostatistics courses. Furthermore, it is useful as a readable, searchable
reference for a data analyst or biostatistician.
7R e f erences
Hamilton, L. C. 2002. Statistics with Stata: Updated for Version 7.P a c i c Grove, CA:
Duxbury Press.
Hills, M. and B. De Stavola. 2002. A Short Introduction to Stata for Biostatistics.
London: Timberlake Consultants Press.
About the Author
John McGready is a research associate in the Johns Hopkins University Bloomberg School of
Public Health Department of Biostatistics.

Review of A Short Introduction to Stata For Biostatistics by Hills and De Stavola

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Review of A Short Introduction to Stata For Biostatistics by Hills and De Stavola

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