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Re: $ERROR and LOGIT

From: Leonid Gibiansky <LGibiansky>
Date: Tue, 15 Dec 2009 11:13:09 -0500

Hi Pavel,
Sorry, I cannot help with the general solution.
For your particular case, you can try several transformations with
various but fixed THETA(10) values (prepare transformed DVs similar to
how you would do it with log-transformation), and then fit transformed
variable with additive error model (is this what you need?). You will
not be able to compare OF, but you may experiment with THETA(10) to
select the one that provides the best fit, and then use it.
Thanks
Leonid

newDV=LOG((VAS/10+THETA(10))/(1-VAS/10+THETA(10))); should be in the
data file
  ...
  model for VAS
  VPRED= prediction of VAS
...
Z=DLOG((VPRED/10+THETA(10))/(1-VPRED/10+THETA(10)))
Y= Z + ERR(1)

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




nonmem
> Leonid,
>
> That is an approximation and approximations can be good. For example,
> if VAS does not approach high values, the exponential model can work
> very well. A modified CV-like model of error (deep modifications)
> supported very reasonable predictions. My proble is not in the
> understanting that a transformation can improve the fit. The problem is
> in implementing it in NONMEM. I have tried about several
> transformations before I sent the email.
>
> Here is the problem: It seems like NONMEM is simlifying the error
> models the same way it simplifies the exponential model of error. The
> question is how to get rid of it. If we cannot use transformations,
> what is the point to use them? My only hope is that some new methods
> implemented in NONMEM7 do not do it.
> (The abstract provides somewhat limited information on what was done.
> Without aditional information, it is hard to replicate.)
>
> Thanks,
> Pavel
>
>
> ----- Original Message -----
> From: Leonid Gibiansky
> Date: Tuesday, December 15, 2009 10:01 am
> Subject: Re: [NMusers] $ERROR and LOGIT
> To: nonmem
> Cc: nmusers
>
> > 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 - 11:13:09 EST

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