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Re: $ERROR and LOGIT

From: Leonid Gibiansky <LGibiansky>
Date: Tue, 15 Dec 2009 09:33:50 -0500

Pavel,
I do not see any justification for a proportional or exponential model:
no reasons to believe that error of measurement is proportional to the
value. I would try simple additive error model. In simulations, one can
truncate at 0 and 10.
The poster that I mentioned specifically discussed various approached to
the problem that you are trying to solve.
Thanks
Leonid


--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566




nonmem
> 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 - 09:33:50 EST

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