Joint International Conference on Long-term Experiments ...

trigger still other responses. In this way, the animal may eventually reach some state of
equilibrium but in the presence of a changed biochemical milieu.
If, now, we continue treating this animal with the 100 % blocking dose for most of its
natural life span, we can expect this changed biochemical milieu to be reflected in the
patterns of lesions that are associated with the aging process. Thus, the treated animals will
tend to have a different syndrome of chronic lesions than untreated controls. If the strains of
rat or mouse chosen are prone to tumor, untreated, then we can expect that this shift in
senile lesions will involve a shift in neoplastic lesions.
Suppose we used some other drug, like e calcium channel blocker, or a monoamine
oxidase (MAO) inhibitor, or any other drug at a dose sufficiently high to produce a
sustained biological effect. The same argument holds, that the resulting change in
biochemical milieu will produce a shift in senile lesions (including tumor patterns).
Suppose, instead of a drug, we use an insecticide, or a dye precursor, or any other agent, but
at a dose sufficiently high ti produce a discernible biological effect (like a 10 % reduction in
weight). The same argument holds.
Thus, we can expect, just from the design of these studies, that the “carcinogenesis”
bioassay will show a treatment-related shift in neoplastic lesions for every compound tested.
MATERIALS AND METHOD
A. Using Data from the “Carcinogenesis”Bioassay
The standard statistical analyses of the “carcinogenesis” bioassay require that we compare
counts of animals with specific tumor types between treatment groups. As a result, the
unsual NTP study involves formal hypothesis tests comparing tumor incidences for 10-15
tumor types, each comparison involving a test of controls versus low dose; controls versus
high dose; two dose-response tests, adjusted for early deaths; and nonlinearity hypothesis
tests per sex per species.
The problem of multiple testing and inflation of the false-positive error rate has had
considerable discussion in the literature. However, there is another side of the problem, the
false-negative error rate. There are only 50 animals per treatment, sex, species, and a
statistical test that compares the number of animals with the a given lesion cannot detect a
slight shift in the probability of lesion. For instance, if the background incidence of a
particular tumor is 20 % and if the treatment reduces that to 7%, half of the studies would
show a “significant” difference and half would not. Thus, the failure to show “significant”
differences between treatments and controls for some of the compounds that have been
tested is no proof that there was no effect. In fact, a rather dramatic increase or reduction in
a particular tumor type could have occurred without producing a “significant” result.
There are ways of strengthening the power of statistical hypothesis tests. One way is to
allow each animal to provide more information. When we compare the counts of animals
with a specific tumor type, each animal provides only 0 or a 1, the least amount of
information available from a single experimental unit. The fact that we look at several
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