From: Michal Figurski <figurski_at_mail.med.upenn.edu>

Date: Thu, 24 Jul 2008 13:25:07 -0400

Date: Thu, 24 Jul 2008 13:25:07 -0400

What are the arguments against fidelity of this concept to scientific validity?

The concept of predictive performance was devised by one of you, biostatisticians - not me! I accept the authoritative view of the person that did it, especially because I do understand it.

When I think of it, excuse my ignorance, it looks to me that this measure summarizes effects of bias, variance, etc, and all the analytical and other errors. Please correct me if I am wrong, but spare me your sarcasm.

-- Michal J. Figurski Bert Gunter wrote:Received on Thu 24 Jul 2008 - 17:29:50 GMT

> To quote (or as nearly so as I can) Einstein's famous remark:

>> "Make everything as simple as possible ... but no simpler">> Moreover, "as possible" here means "maintaining fidelity to scientific> validity," not "simple enough for me to understand." So I don't think a> physicist can explain relativistic cosmology to me (or an organic chemist,> how to synthesize ketones) so that I can understand it without compromising> scientific validity. The onus is then on me to either learn what I need to> know to understand it, or accept the authoritative view of the physicist (or> chemist). I cannot claim ignorance and reject the cosmology because it is> beyond me. That's the "flat earth" philosophy of science, and it is a> terrible obstacle to scientific progress and human enlightenment, in> general.>> Cheers,> Bert Gunter>>> -----Original Message-----> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On> Behalf Of Michal Figurski> Sent: Thursday, July 24, 2008 8:03 AM> Cc: r-help_at_r-project.org> Subject: Re: [R] Coefficients of Logistic Regression from bootstrap - how to> get them?>> Greg and all,>> Just another thought on bias and variability. As I tried to explain, I> perceive this problem as a very practical problem.>> The equation, that is the goal of this work, is supposed to serve the> clinicians to estimate a pharmacokinetic parameter. It therefore must be> simple and also presented in simple language, so that an average> spreadsheet user can make use of it.>> Therefore, in the end, isn't the *predictive performance* an ultimate> measure of it all? Doesn't it account for bias and all the other stuff?> It does tell you in how many cases you may expect to have the predicted> value within 15% of the true value.> I apologize for my naive questions again, but aren't then the> calculations of bias and variance, etc, just a waste of time, while you> have it all summarized in the predictive performance?>> --> Michal J. Figurski>> Greg Snow wrote:

>>> -----Original Message----- >>> From: r-help-bounces_at_r-project.org >>> [mailto:r-help-bounces_at_r-project.org] On Behalf Of Michal Figurski >>> Sent: Wednesday, July 23, 2008 10:22 AM >>> To: r-help_at_r-project.org >>> Subject: Re: [R] Coefficients of Logistic Regression from >>> bootstrap - how to get them? >>> >>> Thank you all for your words of wisdom. >>> >>> I start getting into what you mean by bootstrap. Not >>> surprisingly, it seems to be something else than I do. The >>> bootstrap is a tool, and I would rather compare it to a >>> hammer than to a gun. People say that hammer is for driving >>> nails. This situation is as if I planned to use it to break rocks. >> The bootstrap is more like a whole toolbox than just a single tool. I

> think part of the confusion in this discussion is because you kept asking

> for a hammer and Frank and others kept looking at their toolbox full of> hammers and asking you which one you wanted. Yes you can break a rock with> a hammer designed to drive nails, but why not use the hammer designed to> break rocks when it is easily available.

>>> The key point is that I don't really care about the bias or >>> variance of the mean in the model. These things are useful >>> for statisticians; regular people (like me, also a chemist) >>> do not understand them and have no use for them (well, now I >>> somewhat understand). My goal is very >>> practical: I need an equation that can predict patient's >>> outcome, based on some data, with maximum reliability and accuracy. >> But to get the model with maximum reliability and accuracy you need to

> account for bias and minimize variability. You may not care what those

> numbers are directly, but you do care indirectly about their influence on> your final model. Another instance where both sides were talking past each> other.

>> -- >> Gregory (Greg) L. Snow Ph.D. >> Statistical Data Center >> Intermountain Healthcare >> greg.snow_at_imail.org >> (801) 408-8111

>

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