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Re: $ERROR and LOGIT

From: nonmem
Date: Tue, 15 Dec 2009 11:41:03 +0000 (GMT)

Leonid,

This is about visual analog scale. There are a lot of 0 and 1 values (actually, VAS changes from 0 to 10 in this case, but it can be divided by 10). There are articles, presentatione and dissertations which use logit. So, I try diffrent transformations including logit.

CV error works OK, but I still try to take care of the skewed distribution.

When I use exponential error, NONMEM transforms it into CV error. Later, simulations do not make sense because NONMEM does not do the same. Exactly as described in the nonmem6 manual.

Thank you for the article. I'll keep digging.

Pavel



----- Original Message -----
From: Leonid Gibiansky
Date: Tuesday, December 15, 2009 1:01 am
Subject: Re: [NMusers] $ERROR and LOGIT
To: nonmem
Cc: nmusers

> Pavel,
> I am not sure what is the problem with the log-transformation of
> the
> data. log(x) = infinity only if x = infinity, do you have
> infinite
> observations in your data set? If not, then transformed data
> cannot be
> equal to infinity.
> log(x) = - infinity only if x=0
> do you have BQL observations coded as zeros? If so, you cannot
> use
> exponential error model. But you can either exclude BQLs (and
> use
> log-transformation) or treat them as BQLs (and still use
> log-transformation).
>
> Looks like your prediction F is between 0 and 1. I do not think
> that
> exponential error is appropriate for this type of data. Could
> you
> elaborate what exactly you are modeling? If this is indeed
> interval
> data, this poster can be relevant (Estimating Transformations
> for
> Population Models of Continuous, Closed Interval Data, Matthew
> M.
> Hutmacher and Jonathan L. French):
>
> http://www.page-meeting.org/default.asp?abstract=1463
>
> Thanks
> Leonid
>
> --------------------------------------
> Leonid Gibiansky, Ph.D.
> President, QuantPharm LLC
> web: www.quantpharm.com
> e-mail: LGibiansky at quantpharm.com
> tel: (301) 767 5566
>
>
>
>
> nonmem
> >
> > Hello,
> >
> > NONMEM has the following property related to intra-subject
> variability:>
> > "During estimation by the first-order method, the exponential
> model and
> > proportional models give identical results, i.e., NONMEM
> cannot
> > distinguish between them." So, NONMEM transforms
> F*DEXP(ERR(1)) into F
> > + F*ERR(1).
> >
> > Is there an easy around it? / /I try to code the logit
> transformation.
> > I cannot log-transform the original data as it is suggested in
> some
> > publications including the presentation by Plan and Karlsson
> (Uppsala)
> > because many values will be equal to plus or minus infinity.
> Will
> > NONMEM "linearize" the following code:
> >
> > Z = DLOG((F+THETA(10))/(1-F+THETA(10)))
> > Y = DEXP(Z + ERR(1))/(1 + DEXP(Z + ERR(1)))
> >
> >
> >
> > Thanks!
> >
> > Pavel
> >
> >
> >
>

Received on Tue Dec 15 2009 - 06:41:03 EST

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