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RE: computation of exponential error model for RUV

From: Luann Phillips <Luann.Phillips>
Date: Mon, 19 Jul 2021 16:33:22 +0000


I agree with Jakob except the following:

(1) Altering the value of a prediction, changes the value of the objecti=
ve function value. So DEL should only be used if absolutely necessary.

(2) The current code changes the prediction for every observation. DEL s=
hould only be set if a prediction is actually zero.

(3) If used, DEL should be set equal to 10E-16 (approximately machine ze=
ro). This will prevent large changes in the OBJ value when a predicted valu=
e changes from 0 to a very tiny number such as 10E-15.
Please see suggested code below.

Best regards,
Luann Phillips

;first oral dose F is always zero. Other dose records can also have F=0.
;set DFLAG (dose flag) to prevent log(0)
;Dose records do *not* change the OBJ value so the value of DFLAG does not =
;set a concentration flag (CFLAG). Conc records *do* change the OBJ value.
;change this prediction by as small a value as possible.
;For records with CFLAG=1, check the surrounding data for errors. Is the =
time since previous dose
;excessively long for the compound (incorrect sample date or previous dose =
date)? Does it make sense
;to have an observable concentration at that time point? Etc.
;Using values greater than 10E-16, can result in very large changes in the =
OBJ when a covariate or other
;model change causes the F=0 record to change to a non-zero value which c=
an be as tiny as 10E-15 or less.
IWRES=IRES/W ;exact IWRES for log or additive error model only

From: owner-nmusers
 Of Jakob Ribbing
Sent: Monday, July 19, 2021 12:03 PM
To: Guidi Monia <Monia.Guidi
Cc: nmusers
Subject: Re: [NMusers] computation of exponential error model for RUV

Dear Monia,

Yes, that is correct.
So if one were to use this parameterization for FOCE then one would effecti=
vely get a proportional (symmetric) error distribution, exactly according t=
o what you suggested:

For this reason, the standard approach in NONMEM (for FOCE and exponential =
RUV), is to log-transform both sides, this:


So additive on the log-transformed scale is exactly the error model you wou=
ld like to use.
Maybe that would be a solution for your comparison?

Best wishes


On 19 Jul 2021, at 17:34, Guidi Monia <Monia.Guidi

Dear colleagues,

We would like to compare the NONMEM predictions with those obtained by a Ba=
yesian TDM software for models describing the residual unexplained variabil=
ity with exponential errors.

We need to know if NONMEM performs a first order Taylor expansion of the ex=
ponential error when data are fitted by the FOCE method:
Y=F*EXP(EPS(1)) -> Y= F*(1+EPS(1)).

Could someone help with this?
Thanks in advance

Monia Guidi, PhD

Service of Clinical Pharmacology | University Hospital and University of La=
Center of research and innovation in Clinical Pharmaceutical Sciences | Uni=
versity Hospital and University of Lausanne
BU17 01/193
CH-1011 Lausanne
email: monia.guidi
tel: +41 21 314 38 97

centre hospitalier universitaire vaudois


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Received on Mon Jul 19 2021 - 12:33:22 EDT

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