*The
American Statistician, November 2004, Vol. 58, No. 4*

ActivEpi
1.1 (2nd ed.).

David
G. KLEINBAUM.

Macintosh
and Windows platforms, ISBN: 0-387-21015-6.

ActivEpi
Companion Textbook: A Supplement for Use with the

ActivEpi
CD-ROM.

DavidG.KLEINBAUM, KevinM.
SULLIVAN, and NancyD. BARKER.
New

Students in introductory epidemiology classes often have a wide range
of

backgrounds
in quantitative methods. Although one can teach epidemiology

without
reference to formulas or even to numbers, at a certain point it becomes

necessary
to resort to mathematical symbols. At this point it is important to remember

that
recent educational theory suggests that students learn best by participating

in
their own education rather than by passively receiving information

(Velleman
and Moore 1996).
It is on this educational principle that Kleinbaum’s

introductory
epidemiology text on CD, *ActivEpi*,
is based.

The sequence of 15 chapters follows the order in the Kleinbaum,
Kupper,

and
Morgenstern (1982) book *Epidemiologic
Research *(KKM),
although the

level
of presentation is introductory rather than intermediate as in KKM. Topics

include
study design, measures of effect and impact, validity, and statistical

analysis
of epidemiologic data. Also offered is a review of statistical inference

that
is quite useful to students who have not yet studied statistics or do not

remember
what they learned when they did study it. Information is presented

in
videos, lectures, study questions, interactive quizzes, data analysis
exercises

[using
*Data
Desk*,
a statistical package also used on the *ActivStats*CD(Velleman

2004),
adapted here to the special needs of epidemiological analyses], homework

questions,
and Web links that provide background information on datasets and

refer
students to otherWeb siteswith
epidemiological information. Lectures stop

after
important ideas are presented, and proceed at students’ discretion giving

them
time to consider questions about previous material. For example, within

the
lesson on confounding, after the distinction between confounding and effect

modification
is described, the students are presented with examples that may

include
confounding or effect modification. When the lecture is restarted, the

narrator
discusses each example in detail providing a rationale for each answer.

The
students are thereby allowed to check their understanding of the distinction

between
confounding and effect modification immediately after learning it. It is

this
active partnership between student and imaginary professor that makes the

CD
so useful, particularly for students not accustomed to studying quantitative

topics.

Many chapters contain data-analysis sessions in which the student is
given

a
dataset and required to use Data Desk to apply and interpret a newly learned

analytic
tool. Epidemiology templates allowestimates of
crude and adjusted riskand

odds-based
measures of association, crude and adjusted incidence density

measures,
and etiologic and preventive fractions. Also available is a template

to
analyze matched-pairs, case-control, or cohort studies. Finally, a Data Desk

template
is provided for adjustment of effect measures for exposure or disease

misclassification.

Independent of the format, there are some particularly lucid
expositions that

will
be of great benefit to students. These include an explanation of the
distinction

between
incidence and prevalence and another of the conditions under which

the
rare disease assumption is not required for the odds ratio to approximate

the
rate ratio. Also excellent are Kleinbaum’s
explanations of age adjustment,

selection
bias, confounding involving several risk factors, control of extraneous

factors,
and matching.

One problem with the lectures is that frequent stopping for questions
makes

it
hard to return to a point for later review. This problem is addressed to some

extent
by the extensive index, glossary, and table of contents that places the

reader
at the beginning of the desired section. Another solution to this problem

is
to purchase the accompanying text that closely follows the CD but has all the

advantages
of a book (easy to find specific sections, no computer is needed to

read
it, etc.). I therefore recommend that students buy both the book and the CD.

Another problem results from the need to simplify material for an
introductory

level.
For example, Berkson’s bias is a hospital selection bias based on over

representation of patients with both exposure and disease in hospital
populations.

If the degree of this over-representation is not equal between cases and
controls,

then
an odds ratio from a hospital-based case-control study may be distorted

(Schwartzbaum,
Ahlbom, and Feychting
2003).
However,

Boring
(1989) suggested that Berkson’s bias may not be
present when incident

hospital
cases are used with community controls; yet, in the chapter on selection

bias,
Kleinbaum uses a study of hospital-based lung
cancer cases and community

controls
to illustrate Berkson’s bias. It may be that
cases in Kleinbaum’s example

are
prevalent cases (although prevalent cases are rarely used in case-control

studies)
or that Flanders et al. were not correct and that Berkson’s
bias is actually

present
in this situation. However, even introductory students should be told that

the
presence of Berkson’s bias in the particular
example given is subject to

debate.

The problems cited above, however, are minimal and do not detract from

the
overall richness and creativity of *ActivEpi**
*and
the companion text. The CD

and
book may be used as an adjunct to a class, for self-teaching, or for review.

Overall,
I recommend *ActivEpi**
*because
it is fun to use. A final word to the wise:

check
the ISBN number to make sure that you are buying the most recent version

of
*ActivEpi*.

I would like to acknowledge assistance in reviewing *ActivEpi**
*from
the following

students:
Hoda Jradi, Ryan
Mayes, Nicholas Miceli, Susanne Scott, and

Melissa
Senter.

Judith
SCHWARTZBAUM

*
The **Ohio**
**State**
**University*

**
REFERENCES
**

Differential
Hospitalization Rates in Incident Case-Control Studies,” *Journal
*

*of**
Clinical Epidemiology, *42,
395–401.

Kleinbaum,
D., Kupper, L., and Morgenstern, H. (1982), *Epidemiologic
Research:
*

*Principles
and Quantitative Methods*,

Schwartzbaum,
J., Ahlbom A., and Feychting
M. (2003), “Berkson’s Bias Reviewed,”

*European
Journal of Epidemiology*,
18, 1109–1112.

Velleman,
P. (2004), *ActivStats**
2003–2004 Release*,

Wesley.

Velleman,
P., and Moore, D. (1996), “Multimedia for Teaching Statistics:

Promises
and Pitfalls,” *The
American Statistician*,
50, 217–225.