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Re: More Questions about analysing ABMs
From: |
Docgca |
Subject: |
Re: More Questions about analysing ABMs |
Date: |
Mon, 14 Aug 2000 19:50:44 EDT |
Steve,
Thanks so much for your comments. I would really appreciate it if you could
send me that paper you mentioned. Now for some responses to your comments:
You wrote: I always avoid comparing the distribution of results from multiple
model
replicates (varying only in random number seed) to distributions of
observed data- for this comparison to be valid, your clinical data would
have to be collected from very very similar injuries. When you compare
distributions of model results to data, you have to make sure the same
process was causing the variation in both cases. Rarely in nature do
"experiments" have the same initial conditions like replicate model runs
do.
I guess what I'm trying to do with the RNGs is to model the heterogeniety of
the patient population/response to a fixed insult. The problem with clinical
studies is that there is too much difference in the initial conditions (read:
patients entered into the study) so that they have a lot of trouble getting
enough "n" to see if there is any statistical difference. As a result a lot of
these studies show "no statistical significant results," bue do have
"tendencies." That's a little different than what I'm tryiing to do in this
phase of model testing with the same initial injury and the RNGs on: I think
this is determining the "noise" that comes from the RNGs. Am I wrong in this
assumption? In my paper I showed a graph of a "representative run." The
reviewers had asked for some determination that the "representative" run I
displayed was not something I specifically picked and/or was an outlyer. I had
hoped to show that the run I picked fell in the peak of the distribution !
at a single initial injury number, and it does.
You wrote:
Some things to worry about are that the statistical
significance of the difference between scenarios (with, without
intervention) depends on the number of model runs. Even if your
intervention has only a very small effect, it will be a statistically
significant one if you compare results of 1000 model runs. So be sure to
examine the medical significance as well as the statistical
significance.
This gets back to the purpose of the simulation. Does a very high "n' mean
that you can prove anything statistically significant? That's actually not too
bad, since a lot of these interventions actually hurt more than they help; if
that could be identified prior to trying it on people it might be nice to know.
Thanks again for your comments, and I look forward to your response. And if
you could send me that paper I would really appreciate it.
Yours,
Gary An, MD
Department of Trauma, Cook County Hospital
Chicago, IL
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