>I have prepared an article on the misuse of statistics in medical
> research. If the subject is of interest to you or if you work with
[quoted text clipped - 5 lines]
>
> Fabrikant
Some of your complaints, such as the misuse of relative risk and the quite
small benefits from certain medical interventions are valid. But this does
not look right to me ---
"Now let us see, how the study results deal with the pitfalls mentioned in
the previous section. We do not have to deal here with stability of the
basic data, because on the page 784 of the publication [3] it is mentioned
that at three years of study, 81% of patients in perindopril group were
actually taking the medication. This means, that 19% of the perindopril
group did not take the medication and de-facto belonged to the placebo
group. The number of patients in perindopril group has to be re-calculated
as n2=6110*0.81=4950 and the difference of 1160 patients added to the
placebo group, so that n1=6108+1150=7268. The percentages are computed as
p1=603/7268=8.3% and p2=488/4950=9.9%. Now the placebo group is safer than
the perindopril group and their difference p1-p2= -1.6%, according to
formula (1), is statistically significant with the confidence interval
greater than 99.7%. Does this prove that perindopril is dangerous for the
health? Of course, not, but it does prove, just how shaky is the basis, on
which the authors of the study made their conclusions. "
Those patients not taking the drug at three years would presumably have
taken the drug for a while, and it destroys the whole point of randomisation
to later transfer them to the placebo group. It CANNOT be done. It is
proper to include them in the "treated" arm, even if doing so actually has
the effect of diluting any benefits from taking the drug.
Dilutes the benefits? Yes. You are deceptively including them in the
placebo group without knowing, or allowing for, what their primary outcomes
were. If these patients showed the same morbidity as the rest of the
placebo arm then the figure of 603 in the above calculation is quite wrong -
it should be increased by (9.9% of 1160) = 116), making a mockery of your
calculations.
You are also quite wrong about breast cancer. Mortality rates from breast
cancer are clearly declining even as the incidence of breast cancer
continues to increase.
Peter Moran. .
> I have prepared an article on the misuse of statistics in medical
> research. If the subject is of interest to you or if you work with
[quoted text clipped - 3 lines]
> To read the article, click here:
> http://www.geocities.com/benny_patrick/stats.html
this is for alt.support.cancer readers.
He posted (same) to sci.med.diseases.cancer.
There's more exchanges over there including three (so far) doctors.
J