From: Leonid Gibiansky <*LGibiansky*>

Date: Tue, 15 Dec 2009 00:26:42 -0500

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 - 00:26:42 EST

Date: Tue, 15 Dec 2009 00:26:42 -0500

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

Received on Tue Dec 15 2009 - 00:26:42 EST