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dose of our new drug which will lower diastolic blood pressure an average
of 20 mm Hg.” But this hypothesis is imprecise. What if the necessary
dose of the new drug required taking a tablet every hour? Or caused liver
malfunction? Or even death? First, the researcher would conduct a set of
clinical trials to determine the maximum tolerable dose (MTD) and then
test the hypothesis, “For males over 40 suffering from chronic hyperten-
sion, a daily dose of one-third to one-fourth the MTD of our new drug
will lower diastolic blood pressure an average of 20 mm Hg.”
A BILL OF RIGHTS
•
Scientists can and should be encouraged to make subgroup
analyses.
•
Physicians and engineers should be encouraged to make decisions
utilizing the findings of such analyses.
•
Statisticians and other data analysts can and should rightly refuse to
give their imprimatur to related tests of significance.
In a series of articles by Horwitz et al. [1998], a physician and his col-
leagues strongly criticize the statistical community for denying them (or so
they perceive) the right to provide a statistical analysis for subgroups not
contemplated in the original study protocol. For example, suppose that in
a study of the health of Marine recruits, we notice that not one of the
dozen or so women who received the vaccine contracted pneumonia. Are
we free to provide a
p
value for this result?
Statisticians Smith and Egger [1998] argue against hypothesis tests of
subgroups chosen after the fact, suggesting that the results are often likely
to be explained by the “play of chance.” Altman [1998b, pp. 301-303],
another statistician, concurs noting that “. . . the observed treatment effect
is expected to vary across subgroups of the data . . . simply through chance
variation” and that “doctors seem able to find a biologically plausible
explanation for any finding.” This leads Horwitz et al. [1998] to the
incorrect conclusion that Altman proposes we “dispense with clinical
biology (biologic evidence and pathophysiologic reasoning) as a basis for
forming subgroups.” Neither Altman nor any other statistician would
quarrel with Horwitz et al.'s assertion that physicians must investigate
“how do we [physicians] do our best for a particular patient.”
Scientists can and should be encouraged to make subgroup analyses.
Physicians and engineers should be encouraged to make decisions based
upon them. Few would deny that in an emergency, satisficing [coming up
with workable, fast-acting solutions without complete information] is
better than optimizing.
1
But, by the same token, statisticians should not
1
Chiles [2001, p. 61].
