Re: Medical Research
- From: hrubin@xxxxxxxxxxxxxxxxxxxx (Herman Rubin)
- Date: 14 May 2006 20:51:46 -0400
In article <GLs9g.147559$oL.139680@attbi_s71>,
Skeptic <bcs002b@xxxxxxxxx> wrote:
"Herman Rubin" <hrubin@xxxxxxxxxxxxxxxxxxxx> wrote in message
news:e45cn7$ipa@xxxxxxxxxxxxxxxxxxxxxxx
In article <ex%8g.726475$084.390023@attbi_s22>,
Skeptic <bcs002b@xxxxxxxxx> wrote:
"george conklin" <george@xxxxxxx> wrote in message
news:Ii%8g.3447$u4.1926@xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
The New York Times today has a long article on breast cancer research.
It
seems that current recommendations for chemotherapy were made before
anyone bothered to look at estrogen dependent tumors and non-estrogen
dependent tumors. Looking at the data with the one new variable, it
turns
out that most of the benefits of the chemotherapy were from those who
had
non-estrogen dependent tumors.
But guess what: the hidebound medical business states that since this
one
variable was not thought of IN ADVANCE, the results of the actual
chemotherapy sessions do not meet the 'gold standard,' and thus they
want
to start all over again. Now that is massive stupidity, but guess what,
that is what is going to happen.
It definitely is massive stupidity; I do know the origin
of this type of restriction on jumping to conclusions, and
if done recklessly, MAJOR errors will be made.
You don't understand because you don't understand what is meant by a
scientific study or proof or study design.
He understands SOME of it; it is the medical profession
which does not understand how to use statistics. The
practitioners have had beginning cookbook courses which
do not get into the foundations at all; they get recipes,
but do not know whether they are cooking fish or fowl.
Most MD's use statisticians for their statistical analyses when publishing.
If they do, they use bad ones. I doubt that many of our
students who leave with an applied master's degree, and
that does not get into the foundations, would not have
used some sort of a probit or logit study on the PROSPER
data, which does not take into account the beginning levels
of LDL and HDL in a quantitative manner. That data should
be totally reanalyzed in a sensible manner.
There are some breakdowns not usually given, Even these
indicate that the analysis is poor.
Statistical decision theory is not that old, but is not
often even taught. The problems need to be approached
as what action to take, not whether the effect is
"statistically significant"; that term, which is ancient,
needs to be eradicated, as it tells me nothing of
importance. The p value, by itself, is misleading.
Suffice it to say, each
oncologist is free to decide for him/herself if chemo or hormone treatment
is the way to go.
I have a great objection to this. Each oncologist needs
to inform the patient of the known risks and benefits,
and to give a probability assessment of the costs and
benefits of any available treatment, taking into account
the individual patient. Then the patient should decide
what action to take, based on his or her individual
weights of importance, and also individual contribution
to the assessment of probabilities.
I agree. The patient makes the ultimate decision and needs have the
necessary to make that decision. In actual practice, patients most commonly
ask the doctor what they recommend and follow that advice. So while your
theory of the patient making the decision is, of course, correct. In
practice it will most commonly be the recommendation of the doctor that
decides treatment. Thus, treatment can and will vary from practice to
practice.
The doctors should be required to give the information on
the various types of treatments available. Alas, they
rarely provide any such information, and none of it other
than qualitative.
However, until the proper studies are done, they must
inform their patients that although such a treatment is felt to be
effective, there have not been any studies to prove it.
One cannot "prove" anything with statistics,
Things are proven with studies, which happen to use statistics.
They are not; anyone who believes that does not understand
statistics. Statistics can only change the odds of the
various types of possibilities. The odds can become so
overwhelming that we may say something is proved.
In any case, the standard use of statistical significance
cannot even be translated directly into odds. That null
hypothesis is always false; how much water one drinks has
an effect on diabetes or cancer, which is probably small.
Statistical significance says absolutely nothing about the
magnitude of the effect, nor does its lack.
Whether a treatment is good is not changed by collecting
data, and intelligent decision making will cause the use of
many treatments before that much information is present.
Suppose you had a disease which was about 50% lethal, and
you gave a million people each a placebo or a treatment.
Suppose the difference of the survival rates was 1% in
favor of the treatment. That is extremely significant, so
much so that one could say it was proved that the treatment
had an effect. I suggest that an experimental treatment
which has cured three out of three is a better bet,
although this is not statistically significant.
and can
definitely not prove that a treatment has no effect.
One can get information to better assess the risks and
benefits.
While I don't treat breast cancer, I do deal much with prostate cancer.
There are plenty of comparisons. We treat many men witth hormonal
therapy.
Many of the studies out there are old before newer diagnostic and
treatment
techniques were available. Those tests showed that early hormonal
treatment
made no difference vs. later hormonal treatment, so we might as well wait
for a patient to become symptomatic before giving a treatment with
significant side effects. Newer studies have shown, however, that early
hormone treatment probably does help in a selected patient population.
We'll probably NEVER be able to prove this because those older studies,
done
in the '70's, would be deemed unethical today. So we have studies that
suggest a difference but can't prove it. What do we do? Most oncologists
who treat prostate cancer would start early hormone therapy in those
patients who will probably benefit from it. We're now waiting on a large
European study which, though it won't be exactly what we need, will
probably
help settle some of the debate.
This is the problem with MUCH of medicine. There are
ways to assess the current state of the information, and
act on it. Of course, mistakes will be made, as they are
being made now.
I know you don't like much of medicine, but if you have a better way to
get
at the truth in such matters we'd love to hear it. Things move slowly in
medicine, and for a good reason. For every new treatment with a small
study
that shows that it works... and goes on to be a good treatment, there will
be 10 that eventually end in failure or worse. We can't just jump at
every
fad.
This is the case; things like this were the reason why
one cannot just look at a study and see what else comes
out. However, one can do much more than is being done
now, but nowhere near what we would like.
We need better studies, and more data collected on each
study.
Absolutely.
<> Continuous variables should be used as such, and
<> models based on biology used more often. All of the
<> studies I have seen on the use of statins are horribly
<> flawed; they do not take into account the actual
<> concentrations of the various lipids. As of this time,
<> there is not a single reasonable study showing that the
<> use of a low fat diet is beneficial.
<> I do not know how much followup can be done on drugs. A
<> physician is supposed to report adverse events reported
<> to him; how much is done? Also, what is adverse? I doubt
<> that the posters on this newsgroup will agree on which
<> actions of a particular drug are adverse. Some only occur
<> when the drug is started, or when dosage is changed, and
<> go away. I have seen little of this in the PDR material.
<> What can be easily done about these? One, more data can,
<> and should, be collected, and not just on drug trials.
<> The problem is that this can be very difficult, and the
<> opinions of physicians is VERY difficult to quantify.
<> If we had to do tests for all of this, the cost would be
<> very high.
<> Everyone needs to understand the concepts of probability
<> and statistical decision theory, but they do not need to
<> know how to carry out the calculations. Problems need to
<> be formulated first.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Department of Statistics, Purdue University
hrubin@xxxxxxxxxxxxxxx Phone: (765)494-6054 FAX: (765)494-0558
.
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