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


ActivEpi 1.1 (2nd ed.).

David G. KLEINBAUM. New York : Springer, 2004, $69.95 (CD-ROM),

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

  York : Springer, 2003, ix + 518 pp., $39.95 (P), ISBN: 0-387-95574-7.


     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 ActivStatsCD(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


     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


     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, Flanders , Boyle, and

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


     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



Flanders , W. D., Boyle, C. A., and Boring, J. R. (1989), “Bias Associated with

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, New York : Van Nostrand Reinhold.

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, New York : Addison-


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

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

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