[From Rick Marken (2007.01.01.1135)]
David Goldstein (2007.01.01.0910)
�
Dear Bill and listmates,
�
If we take a well analyzed task from PCT, say the pursuit tracking task, what does 'understanding the mechanism' mean?
To me, it means having a model that behaves exactly like as the person does under the same circumstances.
How can we study an individual to find out what the parameters mean in the results?
I'd say that it's by using models. We see how variations in the model parameters affect the behavior of the model and, assuming the model behaves just like the person, infer that variations in corresponding parameters in the person would affect the person's behavior in the same ay.
What kinds of variables of task and subject-state change the results of the parameters?
I don't think that can be answered without knowing what model is assumed.
What is going on in different parts of the person's nervous system as the person does the task as reavealed by functional mri?
I think you would have to do research to find out.
And how are we going to do this without using statistics, even for the single case study?
I think you will always have to use measures that are basically descriptive statistics. The data we collect is really only a sample of behavior and the relationships we measure are, therefore, sample statistics. All measurements in science are like this. There is also error in measurement that affects the accuracy of your sample findings. But when the data are consistent enough -- as they are in the physical sciences and in PCT research -- you don't really need to use inferential statistical methods. You can just evaluate the results in terms of how well they fit are fit by your model. So if the deviation of observed data from model behaivor is less than, say, 3% of the total variation in the data then you're probably on the right track.
Best wishes to all for 2007,
David
Same to you,boobalah!
Best
Rick
�
From: Control Systems Group Network (CSGnet) [mailto:CSGNET@LISTSERV.UIUC.EDU] On Behalf Of Bill Powers
Sent: Monday, January 01, 2007 8:37 AM
To: CSGNET@LISTSERV.UIUC.EDU
Subject: Re: Suggested Projects for Class on PCT
[From Bill Powers (2007.01.01.0540 MST)]
Very hard to get the fingers to type 2007. Happy new year to all.
Martin Taylor 2006.12.31.22.57 --
I think you might enjoy the attached, from the current issue of American Scientist.
Thanks. Most interesting. One of the remarkable features of this paper is that it shows how to improve treatments, on the average, without knowing ANYTHING AT ALL about what is wrong with the patient. By examining symptoms, one can determine risk factors for subgroups, and then use the results of clinical trials within subgroups to determine treatments. This gives results that are clearly better, on the average, than using just one measure of past outcomes for everyone.
Phil Runkel has pointed out that subdividing a population has its own risk factor, in that this strategy reduces the numbers within each subgroup and increases the standard deviation of any measure. He calls this "fine slicing."
There is a spectrum of fine slicing with the whole group at one end and a single individual at the other end. Statistical analysis does no good at all with single individuals -- however any judgment about an individual is stated on the basis of group statistics, it inevitably includes terms like "in the long run" or "on the average" or "typically" which refer to the whole population, not one person. The probably of a single event is zero if it doesn't happen, and 1 if it does happen, and you don't know which is going to be the case if there is to be only a single event (this patient's experience of the treatment).
Obviously, the larger the clinical study is, the smaller is the average effect of a treatment that can be shown to be significant. But by the same token, the larger the clinical study needed to achieve significance becomes, the less significant must the measured effect in any given size of subgroup. The Am Sci paper shows that there is a tradeoff, with improvements being achievable when the subgroups are based on estimated risks. But there must be some number of subgroups at which the increasing uncertainty of measurement offsets the gain due to segregating people into a larger number of risk groups. And way over at one end of the spectrum, where the doctor must decide on the treatment of a single individual and the individual must decide whether to allow it, even the improved forecasting does that individual no good at all (except perhaps to make the person feel better about the prognosis, which of course is a population prediction).
My complaint is not that we shouldn't do studies like these and use statistics to try to improve treatments. When that is all we have, it would be foolish not to do what is possible. My complaint is that until quite recently, that statistical approach to treatment has been essentially all we do have, most particularly at the level of patient treatment as opposed to research. A very large part of the "education" of a general practitioner comes from the salesmen representing drug companies, the "detail men" (If women insist on being included in this dubious group, I will amend the wording, but I have yet to hear a feminist complaining about phrases like "the evil men do."). And the drug companies, by and large, rely far more on statistics than on science.
I see hints here and there that a new movement is afoot, called (I think) "systems biology." I've been advocating this for years, but most of the money spent on research into cures has been of the old statistical kind, with very little support for trying to understand how the damned thing works. Systems biology, as I understand the hints, is aimed at dealing with all the variables that are simultaneously interacting in a living system, instead of looking for simplistic causal relationships between one variable and a whole complex of symptoms.
The systems approach is what we need to deal with people at the specimen level. It's what we need in order to say what is wrong with someone who exhibits the symptoms we carelessly label with words like "schizophrenia" or "measles" or "headaches." It's what we need to start recognizing that side-effects result from real and important changes in variables in the whole system that are caused by the treatment. You can't affect just one thing in a system.
We have to get beyond the statistical approach before we can start trying to say what is wrong with a person who exhibits certain symptoms. "Depression" is not a name for a disorder, it's a name for a set of symptoms that arises from some as yet unknown underlying malfunction. The same holds true for most of the other names we give to maladies. Because putative "sciences of life" have relied so heavily on statistics, what they think they know about the human system is at a terribly superficial level, like the level of the cargo cultists trying to bring prosperity back by cutting crude landing strips through the jungle. Treating symptoms is what we do before we have science. The basic premise of statistics is that the future will (for no reason at all) be like the past, which is no way to get anywhere.
Best,
Bill P.
�
Richard S. Marken Consulting
marken@mindreadings.com
Home 310 474-0313
Cell 310 729-1400
···
-----Original Message-----