Dear NONMEM users,
We have noticed some problems with the table outputs of RES and WRES, when we implemented Beal's M3 method as proposed by Ahn and colleagues (J Pharmacokinet Pharmacodyn (2008) 35:401–421). While RES and WRES are correctly calculated and reported for patients without BLOQ records, these metrics are reported as being 0 for patients with at least one BLOQ sample, even for the records that were not flagged as BLOQ. This behavior seems to be common to NONMEM 6 and 7.
Does anybody know about an NONMEM option or a workaround that would allow the user to access to the actual RES and WRES for the non-BLQ records?
Any feedback would be greatly appreciated.
Sebastien Bihorel
PS: this is the $ERROR block code we used for a simple proportional RV model
$ERROR (ONLY OBSERVATIONS)
;Information needed for BLQF and > BLQF samples
LOQ=5
SIG = THETA(3)
DFLG=0 ;create a dose record flag
IF(AMT.NE.0) DFLG=1
IPRED=F+DFLG
W=IPRED
;Computations for samples with DV > LOQ (BLQF=0)
IF (BLQF.LT.0.25) THEN
F_FLAG=0
FFLG=0
IRES=DV-IPRED
IWRES=IRES/W
Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
ENDIF
;Computations for samples with DV <= LOQ (BLQF=1)
IF (BLQF.GE.0.25) THEN
F_FLAG=1
FFLG=1
DUM=(LOQ-IPRED)/(W*SIG)
IPRED=PHI(DUM)
Y=IPRED ;NOTE: prediction is a *probability*
ENDIF
RES and WRES output with Beal's M3 method
Dear Sebastien and NONMEM users,
It is correct that NONMEM doesn't return any RES or WRES for individuals who
have at least one observation with F_FLAG=1. It is understandable that
NONMEM can't return residuals for the BQL observations (F_FLAG=1) since PRED
is a likelihood in this case and not a prediction (furthermore the
observation is an interval and can neither be used to calculate a residual).
However, I don't understand why it doesn't do so for the observations for
which F_FLAG=0? Can this be considered a bug?
You can of course always get out individual residuals (IRES) and individual
weighted residuals (IWRES) (below this paragraph you can see my definition
of IWRES). What you can do if you really want to get RES and WRES is to run
a MAXEVAL=0 run with your final estimates. The BQL data should in this case
be omitted (or set to a fix value) and all M3 code taken out. Remember in
this case that the IRES and IWRES and other EBE dependent variables will not
be correct following the MAXEVAL=0 run (i.e. use only the PRED, RES and WRES
out of this run and the rest from your original fit with the M3 code). In
case you are using the FOCE estimation method you would preferably want to
look at conditional weighted residuals (CWRES) rather than WRES. However I
have yet not seen any example of a workaround to get these when using the M3
method (Obs! CWRES will not be correctly calculated with the MAXEVAL=0
trick).
$ERROR
IPRED = F
W = THETA(11) ; SD for additive
residual error
; W = THETA(11) * IPRED ; SD for proportional
residual error
; W = SQRT(THEATA(11)**2+THETA(12)**2*IPRED**2) ; SD for combined
residual error
Y = IPRED + W*EPS(1)
IRES = DV - IPRED
IWRES = IRES/W
$SIGMA 1 FIX
If you use Xpose to do your diagnostic plots it can be good to know that
Xpose by default omits all rows with WRES=0 (this is done to omit
information on dosing row from being plotted). You can change this setting
in Xpose by using the command inclZeroWRES=TRUE (you should then enter some
other subset to omit the dose rows e.g. subset="EVID==0").
My favorite type of diagnostics are VPCs (I think I share this with Prof.
Nick Holford). VPCs can easily be adopted to handle the presence of BQL
data. How to do this with PsN and Xpose is explained in a PAGE poster from
2009
( http://www.page-meeting.org/pdf_assets/7002-Poster_PAGE_VPC_090618_final.pd
f).
Best regards,
Martin Bergstrand, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences,
Uppsala University
-----------------------------------------------
[email protected]
-----------------------------------------------
Work: +46 18 471 4639
Mobile: +46 709 994 396
Fax: +46 18 471 4003
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Sebastien Bihorel
Sent: den 7 april 2010 17:31
To: [email protected]
Subject: [NMusers] RES and WRES output with Beal's M3 method
Dear NONMEM users,
We have noticed some problems with the table outputs of RES and WRES,
when we implemented Beal's M3 method as proposed by Ahn and colleagues
(J Pharmacokinet Pharmacodyn (2008) 35:401-421). While RES and WRES are
correctly calculated and reported for patients without BLOQ records,
these metrics are reported as being 0 for patients with at least one
BLOQ sample, even for the records that were not flagged as BLOQ. This
behavior seems to be common to NONMEM 6 and 7.
Does anybody know about an NONMEM option or a workaround that would
allow the user to access to the actual RES and WRES for the non-BLQ records?
Any feedback would be greatly appreciated.
Sebastien Bihorel
PS: this is the $ERROR block code we used for a simple proportional RV model
$ERROR (ONLY OBSERVATIONS)
;Information needed for BLQF and > BLQF samples
LOQ=5
SIG = THETA(3)
DFLG=0 ;create a dose record flag
IF(AMT.NE.0) DFLG=1
IPRED=F+DFLG
W=IPRED
;Computations for samples with DV > LOQ (BLQF=0)
IF (BLQF.LT.0.25) THEN
F_FLAG=0
FFLG=0
IRES=DV-IPRED
IWRES=IRES/W
Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
ENDIF
;Computations for samples with DV <= LOQ (BLQF=1)
IF (BLQF.GE.0.25) THEN
F_FLAG=1
FFLG=1
DUM=(LOQ-IPRED)/(W*SIG)
IPRED=PHI(DUM)
Y=IPRED ;NOTE: prediction is a *probability*
ENDIF
Historical perspective.
This issue was discussed with Stuart Beal. He believed that weighted
residuals would be incorrect for an individual that had both continuous
dependent variables and a likelihood in the calculation of their
contribution to the objective function value, as is the case with his M3
or M4 BQL methods The code for both RES and WRES are intentionally
bypassed in these cases.
Tom
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]]
On Behalf Of Sebastien Bihorel
Sent: Wednesday, April 07, 2010 1:57 PM
To: Martin Bergstrand
Cc: 'Sebastien Bihorel'; [email protected]
Subject: Re: [NMusers] RES and WRES output with Beal's M3 method
Hi Martin,
Thanks for your reply. I tend to agree with you this should be
considered as a bug.
I tried different options to access to the actual RES and WRES inside
the $ERROR block. The best I could do so far was to access to PRED
values using (COMACT.EQ.1). With this call, I can assign PRED values to
a PPRED variable that I used to compute some residuals PRES (DV-PPRED)
and some weighted residuals PWRES (RES/W) using your defining of W. This
provides some non-zero values for non-BLOQ samples. By checking PRES and
PWRES against RES and WRES for patients without BLOQ samples, it appears
that PWRES is correct as long as I don't include inter-individual
variability in my model (PRES seems to be always correct).
Any idea how the formula for PWRES should be altered?
Sebastien
New code:
$ERROR (ONLY OBSERVATIONS)
;Information needed for BLQF and > BLQF samples
LOQ=5
SIG = THETA(3)
DFLG=0 ;create a dose record flag
IF(AMT.NE.0) DFLG=1
IPRED=F+DFLG
W=IPRED*SIG
IF (COMACT.EQ.1) PPRED=F+DFLG
PRES=DV-PPRED
PWRES=PRES/W
;Computations for samples with DV > LOQ (BLQF=0)
IF (BLQF.LT.0.25) THEN
F_FLAG=0
FFLG=0
IRES=DV-IPRED
IWRES=IRES/W
Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
ENDIF
;Computations for samples with DV <= LOQ (BLQF=1)
IF (BLQF.GE.0.25) THEN
F_FLAG=1
FFLG=1
DUM=(LOQ-IPRED)/(W*SIG)
IPRED=PHI(DUM)
Y=IPRED ;NOTE: prediction is a *probability*
ENDIF
Martin Bergstrand wrote:
> Dear Sebastien and NONMEM users,
>
> It is correct that NONMEM doesn't return any RES or WRES for
individuals who
> have at least one observation with F_FLAG=1. It is understandable that
> NONMEM can't return residuals for the BQL observations (F_FLAG=1)
since PRED
> is a likelihood in this case and not a prediction (furthermore the
> observation is an interval and can neither be used to calculate a
residual).
> However, I don't understand why it doesn't do so for the observations
for
> which F_FLAG=0? Can this be considered a bug?
>
> You can of course always get out individual residuals (IRES) and
individual
> weighted residuals (IWRES) (below this paragraph you can see my
definition
> of IWRES). What you can do if you really want to get RES and WRES is
to run
> a MAXEVAL=0 run with your final estimates. The BQL data should in this
case
> be omitted (or set to a fix value) and all M3 code taken out. Remember
in
> this case that the IRES and IWRES and other EBE dependent variables
will not
> be correct following the MAXEVAL=0 run (i.e. use only the PRED, RES
and WRES
> out of this run and the rest from your original fit with the M3 code).
In
> case you are using the FOCE estimation method you would preferably
want to
> look at conditional weighted residuals (CWRES) rather than WRES.
However I
> have yet not seen any example of a workaround to get these when using
the M3
> method (Obs! CWRES will not be correctly calculated with the MAXEVAL=0
> trick).
>
> $ERROR
> IPRED = F
> W = THETA(11) ; SD for additive
> residual error
> ; W = THETA(11) * IPRED ; SD for
proportional
> residual error
> ; W = SQRT(THEATA(11)**2+THETA(12)**2*IPRED**2) ; SD for combined
> residual error
> Y = IPRED + W*EPS(1)
> IRES = DV - IPRED
> IWRES = IRES/W
>
> $SIGMA 1 FIX
>
> If you use Xpose to do your diagnostic plots it can be good to know
that
> Xpose by default omits all rows with WRES=0 (this is done to omit
> information on dosing row from being plotted). You can change this
setting
> in Xpose by using the command inclZeroWRES=TRUE (you should then enter
some
> other subset to omit the dose rows e.g. subset="EVID==0").
>
> My favorite type of diagnostics are VPCs (I think I share this with
Prof.
> Nick Holford). VPCs can easily be adopted to handle the presence of
BQL
> data. How to do this with PsN and Xpose is explained in a PAGE poster
from
> 2009
>
( http://www.page-meeting.org/pdf_assets/7002-Poster_PAGE_VPC_090618_fina
l.pd
> f).
>
> Best regards,
>
> Martin Bergstrand, MSc, PhD student
> -----------------------------------------------
> Pharmacometrics Research Group,
> Department of Pharmaceutical Biosciences,
> Uppsala University
> -----------------------------------------------
> [email protected]
> -----------------------------------------------
> Work: +46 18 471 4639
> Mobile: +46 709 994 396
> Fax: +46 18 471 4003
>
>
> -----Original Message-----
> From: [email protected]
[mailto:[email protected]] On
> Behalf Of Sebastien Bihorel
> Sent: den 7 april 2010 17:31
> To: [email protected]
> Subject: [NMusers] RES and WRES output with Beal's M3 method
>
> Dear NONMEM users,
>
> We have noticed some problems with the table outputs of RES and WRES,
> when we implemented Beal's M3 method as proposed by Ahn and colleagues
> (J Pharmacokinet Pharmacodyn (2008) 35:401-421). While RES and WRES
are
> correctly calculated and reported for patients without BLOQ records,
> these metrics are reported as being 0 for patients with at least one
> BLOQ sample, even for the records that were not flagged as BLOQ. This
> behavior seems to be common to NONMEM 6 and 7.
>
> Does anybody know about an NONMEM option or a workaround that would
> allow the user to access to the actual RES and WRES for the non-BLQ
records?
>
> Any feedback would be greatly appreciated.
>
> Sebastien Bihorel
>
> PS: this is the $ERROR block code we used for a simple proportional RV
model
>
> $ERROR (ONLY OBSERVATIONS)
>
> ;Information needed for BLQF and > BLQF samples
> LOQ=5
> SIG = THETA(3)
>
> DFLG=0 ;create a dose record flag
> IF(AMT.NE.0) DFLG=1
>
> IPRED=F+DFLG
> W=IPRED
>
> ;Computations for samples with DV > LOQ (BLQF=0)
> IF (BLQF.LT.0.25) THEN
> F_FLAG=0
> FFLG=0
> IRES=DV-IPRED
> IWRES=IRES/W
> Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
> ENDIF
>
> ;Computations for samples with DV <= LOQ (BLQF=1)
> IF (BLQF.GE.0.25) THEN
> F_FLAG=1
> FFLG=1
> DUM=(LOQ-IPRED)/(W*SIG)
> IPRED=PHI(DUM)
> Y=IPRED ;NOTE: prediction is a *probability*
> ENDIF
>
>
Hello all,
IWRES, WRES, CWRES, or even Monte Carlo-based population residuals do not
provide great diagnostic value unfortunately for datasets with censored
data. BQL observations influence the fit through the censored likelihood,
but these observations are not represented in the residual diagnostic plots
(they are not defined for the BQL observations). In fact, the population
residual plots should not have a mean of 0, nor a variance of 1 (if
standardized by variance estimates), and are likely to have skewness because
of the conditional nature of their calculation. The degree of departure
from these typical target values depends upon the number of BQL observations
of course. Also, these residuals will look worse using M3 method (a more
principled approach) compared to those derived from fitting using method M1,
which discards BQL observations during estimation. This reflects the bias
in the estimates and predictions induced by excluding the non-ignorable
censored observations (M1), counter to that typically expected from
inspection of residuals. Predictive checks, as Martin suggests, are
probably the only tool for evaluating the model when you have an influential
number of BQL observations. Martin provided a very nice poster on VPCs for
such situations. However, if you wanted to use residuals in a PPC as
opposed to concentration (perhaps a less sufficient statistic), then which
population residual you compute, WRES, CWRES, or Monte Carlo population
residuals might not be all that influential in the model evaluation as long
as you compute the residuals similarly for the original model fit to the
observed data and the model fits to the simulated datasets.
One other thing to consider is outliers. Residual-based determination of
outliers cannot be applied to the observations that are BQL (perhaps the
threshold should be determined from simulations given the expected lack of
normality when censoring is present). However, I would argue that this
doesn't mean that a BQL observation cannot be an outlier. Take for example
a concentration profile that has a BQL observation reported around the time
of TMAX and also that the concentrations before and after the BQL
observation are a reasonable distance (perhaps 4-6 standard deviations away)
from the lower limit of quantification. The likelihood for this BQL
observation is PHI((QL-IPRED)/W) where PHI is the cumulative normal, and
IPRED and W are defined as per Martin below. If IPRED is large relative to
QL, this provides a very negative value of (QL-IPRED)/W, and PHI() will be
near 0. When -2 X log-likelihood is computed, i.e., -2 X log(PHI()), a very
large number will result. For example, if (QL-IPRED)/W = -6, then -2 X
log(PHI(-6)) = 41.5. If we looked at an observation that was not censored
that had an IWRES = -6 (assume approximate %CV = 30%), the -2 X log
likelihood is 33.6. Thus, the QL-IPRED/W might be considered an outlier,
and the estimation procedure might influenced by this BQL observation.
Granted the scenario here suggests that the point might be an outlier
without 'quantifying' its degree. Note that if (QL-IPRED)/W = 6, this
suggest that IPRED is much less than QL. This is likely to occur in the
elimination phase after a few other BQL observations have been observed. In
this case, it is not an issue, because PHI(6) is near 1, and -2 X
log(PHI(6)) is near 0 indicating a negligible contribution to the
likelihood. In my mind, this is along the line of reasoning for proposing
the M6 method, where QL/2 is used to impute the first BQL observation in the
terminal phase. I am less certain about the influence of (QL-IPRED)/W = 6
in the absorption phase, especially if Tlag is estimated.
Best,
Matt
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Martin Bergstrand
Sent: Wednesday, April 07, 2010 1:02 PM
To: 'Sebastien Bihorel'; [email protected]
Subject: RE: [NMusers] RES and WRES output with Beal's M3 method
Dear Sebastien and NONMEM users,
It is correct that NONMEM doesn't return any RES or WRES for individuals who
have at least one observation with F_FLAG=1. It is understandable that
NONMEM can't return residuals for the BQL observations (F_FLAG=1) since PRED
is a likelihood in this case and not a prediction (furthermore the
observation is an interval and can neither be used to calculate a residual).
However, I don't understand why it doesn't do so for the observations for
which F_FLAG=0? Can this be considered a bug?
You can of course always get out individual residuals (IRES) and individual
weighted residuals (IWRES) (below this paragraph you can see my definition
of IWRES). What you can do if you really want to get RES and WRES is to run
a MAXEVAL=0 run with your final estimates. The BQL data should in this case
be omitted (or set to a fix value) and all M3 code taken out. Remember in
this case that the IRES and IWRES and other EBE dependent variables will not
be correct following the MAXEVAL=0 run (i.e. use only the PRED, RES and WRES
out of this run and the rest from your original fit with the M3 code). In
case you are using the FOCE estimation method you would preferably want to
look at conditional weighted residuals (CWRES) rather than WRES. However I
have yet not seen any example of a workaround to get these when using the M3
method (Obs! CWRES will not be correctly calculated with the MAXEVAL=0
trick).
$ERROR
IPRED = F
W = THETA(11) ; SD for additive
residual error
; W = THETA(11) * IPRED ; SD for proportional
residual error
; W = SQRT(THEATA(11)**2+THETA(12)**2*IPRED**2) ; SD for combined
residual error
Y = IPRED + W*EPS(1)
IRES = DV - IPRED
IWRES = IRES/W
$SIGMA 1 FIX
If you use Xpose to do your diagnostic plots it can be good to know that
Xpose by default omits all rows with WRES=0 (this is done to omit
information on dosing row from being plotted). You can change this setting
in Xpose by using the command inclZeroWRES=TRUE (you should then enter some
other subset to omit the dose rows e.g. subset="EVID==0").
My favorite type of diagnostics are VPCs (I think I share this with Prof.
Nick Holford). VPCs can easily be adopted to handle the presence of BQL
data. How to do this with PsN and Xpose is explained in a PAGE poster from
2009
( http://www.page-meeting.org/pdf_assets/7002-Poster_PAGE_VPC_090618_final.pd
f).
Best regards,
Martin Bergstrand, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences,
Uppsala University
-----------------------------------------------
[email protected]
-----------------------------------------------
Work: +46 18 471 4639
Mobile: +46 709 994 396
Fax: +46 18 471 4003
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Sebastien Bihorel
Sent: den 7 april 2010 17:31
To: [email protected]
Subject: [NMusers] RES and WRES output with Beal's M3 method
Dear NONMEM users,
We have noticed some problems with the table outputs of RES and WRES,
when we implemented Beal's M3 method as proposed by Ahn and colleagues
(J Pharmacokinet Pharmacodyn (2008) 35:401-421). While RES and WRES are
correctly calculated and reported for patients without BLOQ records,
these metrics are reported as being 0 for patients with at least one
BLOQ sample, even for the records that were not flagged as BLOQ. This
behavior seems to be common to NONMEM 6 and 7.
Does anybody know about an NONMEM option or a workaround that would
allow the user to access to the actual RES and WRES for the non-BLQ records?
Any feedback would be greatly appreciated.
Sebastien Bihorel
PS: this is the $ERROR block code we used for a simple proportional RV model
$ERROR (ONLY OBSERVATIONS)
;Information needed for BLQF and > BLQF samples
LOQ=5
SIG = THETA(3)
DFLG=0 ;create a dose record flag
IF(AMT.NE.0) DFLG=1
IPRED=F+DFLG
W=IPRED
;Computations for samples with DV > LOQ (BLQF=0)
IF (BLQF.LT.0.25) THEN
F_FLAG=0
FFLG=0
IRES=DV-IPRED
IWRES=IRES/W
Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
ENDIF
;Computations for samples with DV <= LOQ (BLQF=1)
IF (BLQF.GE.0.25) THEN
F_FLAG=1
FFLG=1
DUM=(LOQ-IPRED)/(W*SIG)
IPRED=PHI(DUM)
Y=IPRED ;NOTE: prediction is a *probability*
ENDIF
Thanks Martin, Matt and Scott for your replies, most helpful.
As a side note, my colleagues and I tried to look more into a "manual" way to compute WRES, which appears to be a non trivial task and involves the computation of partial derivatives with respect to the IIV parameters. The detailed equations in NONMEM guide 1 page 41 clearly explained why the computations of WRES simplify to RES/W when no IIV is included in the model.
Sebastien
Matt Hutmacher wrote:
> Hello all,
>
> IWRES, WRES, CWRES, or even Monte Carlo-based population residuals do not
> provide great diagnostic value unfortunately for datasets with censored
> data. BQL observations influence the fit through the censored likelihood,
> but these observations are not represented in the residual diagnostic plots
> (they are not defined for the BQL observations). In fact, the population
> residual plots should not have a mean of 0, nor a variance of 1 (if
> standardized by variance estimates), and are likely to have skewness because
> of the conditional nature of their calculation. The degree of departure
> from these typical target values depends upon the number of BQL observations
> of course. Also, these residuals will look worse using M3 method (a more
> principled approach) compared to those derived from fitting using method M1,
> which discards BQL observations during estimation. This reflects the bias
> in the estimates and predictions induced by excluding the non-ignorable
> censored observations (M1), counter to that typically expected from
> inspection of residuals. Predictive checks, as Martin suggests, are
> probably the only tool for evaluating the model when you have an influential
> number of BQL observations. Martin provided a very nice poster on VPCs for
> such situations. However, if you wanted to use residuals in a PPC as
> opposed to concentration (perhaps a less sufficient statistic), then which
> population residual you compute, WRES, CWRES, or Monte Carlo population
> residuals might not be all that influential in the model evaluation as long
> as you compute the residuals similarly for the original model fit to the
> observed data and the model fits to the simulated datasets.
>
> One other thing to consider is outliers. Residual-based determination of
> outliers cannot be applied to the observations that are BQL (perhaps the
> threshold should be determined from simulations given the expected lack of
> normality when censoring is present). However, I would argue that this
> doesn't mean that a BQL observation cannot be an outlier. Take for example
> a concentration profile that has a BQL observation reported around the time
> of TMAX and also that the concentrations before and after the BQL
> observation are a reasonable distance (perhaps 4-6 standard deviations away)
> from the lower limit of quantification. The likelihood for this BQL
> observation is PHI((QL-IPRED)/W) where PHI is the cumulative normal, and
> IPRED and W are defined as per Martin below. If IPRED is large relative to
> QL, this provides a very negative value of (QL-IPRED)/W, and PHI() will be
> near 0. When -2 X log-likelihood is computed, i.e., -2 X log(PHI()), a very
> large number will result. For example, if (QL-IPRED)/W = -6, then -2 X
> log(PHI(-6)) = 41.5. If we looked at an observation that was not censored
> that had an IWRES = -6 (assume approximate %CV = 30%), the -2 X log
> likelihood is 33.6. Thus, the QL-IPRED/W might be considered an outlier,
> and the estimation procedure might influenced by this BQL observation.
> Granted the scenario here suggests that the point might be an outlier
> without 'quantifying' its degree. Note that if (QL-IPRED)/W = 6, this
> suggest that IPRED is much less than QL. This is likely to occur in the
> elimination phase after a few other BQL observations have been observed. In
> this case, it is not an issue, because PHI(6) is near 1, and -2 X
> log(PHI(6)) is near 0 indicating a negligible contribution to the
> likelihood. In my mind, this is along the line of reasoning for proposing
> the M6 method, where QL/2 is used to impute the first BQL observation in the
> terminal phase. I am less certain about the influence of (QL-IPRED)/W = 6
> in the absorption phase, especially if Tlag is estimated.
>
> Best,
> Matt
>
Quoted reply history
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of Martin Bergstrand
> Sent: Wednesday, April 07, 2010 1:02 PM
> To: 'Sebastien Bihorel'; [email protected]
> Subject: RE: [NMusers] RES and WRES output with Beal's M3 method
>
> Dear Sebastien and NONMEM users,
>
> It is correct that NONMEM doesn't return any RES or WRES for individuals who
> have at least one observation with F_FLAG=1. It is understandable that
> NONMEM can't return residuals for the BQL observations (F_FLAG=1) since PRED
> is a likelihood in this case and not a prediction (furthermore the
> observation is an interval and can neither be used to calculate a residual).
> However, I don't understand why it doesn't do so for the observations for
> which F_FLAG=0? Can this be considered a bug?
>
> You can of course always get out individual residuals (IRES) and individual
> weighted residuals (IWRES) (below this paragraph you can see my definition
> of IWRES). What you can do if you really want to get RES and WRES is to run
> a MAXEVAL=0 run with your final estimates. The BQL data should in this case
> be omitted (or set to a fix value) and all M3 code taken out. Remember in
> this case that the IRES and IWRES and other EBE dependent variables will not
> be correct following the MAXEVAL=0 run (i.e. use only the PRED, RES and WRES
> out of this run and the rest from your original fit with the M3 code). In
> case you are using the FOCE estimation method you would preferably want to
> look at conditional weighted residuals (CWRES) rather than WRES. However I
> have yet not seen any example of a workaround to get these when using the M3
> method (Obs! CWRES will not be correctly calculated with the MAXEVAL=0
>
> trick).
>
> $ERROR
> IPRED = F
> W = THETA(11) ; SD for additive
>
> residual error ; W = THETA(11) * IPRED ; SD for proportional
>
> residual error
> ; W = SQRT(THEATA(11)**2+THETA(12)**2*IPRED**2) ; SD for combined
> residual error
> Y = IPRED + W*EPS(1)
> IRES = DV - IPRED
> IWRES = IRES/W
>
> $SIGMA 1 FIX
>
> If you use Xpose to do your diagnostic plots it can be good to know that
> Xpose by default omits all rows with WRES=0 (this is done to omit
> information on dosing row from being plotted). You can change this setting
> in Xpose by using the command inclZeroWRES=TRUE (you should then enter some
>
> other subset to omit the dose rows e.g. subset="EVID==0").
>
> My favorite type of diagnostics are VPCs (I think I share this with Prof.
> Nick Holford). VPCs can easily be adopted to handle the presence of BQL
> data. How to do this with PsN and Xpose is explained in a PAGE poster from
> 2009
> ( http://www.page-meeting.org/pdf_assets/7002-Poster_PAGE_VPC_090618_final.pd
> f).
>
> Best regards,
>
> Martin Bergstrand, MSc, PhD student
> -----------------------------------------------
> Pharmacometrics Research Group,
> Department of Pharmaceutical Biosciences,
> Uppsala University
> -----------------------------------------------
> [email protected]
> -----------------------------------------------
> Work: +46 18 471 4639
> Mobile: +46 709 994 396
> Fax: +46 18 471 4003
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of Sebastien Bihorel
> Sent: den 7 april 2010 17:31
> To: [email protected]
> Subject: [NMusers] RES and WRES output with Beal's M3 method
>
> Dear NONMEM users,
>
> We have noticed some problems with the table outputs of RES and WRES, when we implemented Beal's M3 method as proposed by Ahn and colleagues (J Pharmacokinet Pharmacodyn (2008) 35:401-421). While RES and WRES are correctly calculated and reported for patients without BLOQ records, these metrics are reported as being 0 for patients with at least one BLOQ sample, even for the records that were not flagged as BLOQ. This behavior seems to be common to NONMEM 6 and 7.
>
> Does anybody know about an NONMEM option or a workaround that would allow the user to access to the actual RES and WRES for the non-BLQ records?
>
> Any feedback would be greatly appreciated.
>
> Sebastien Bihorel
>
> PS: this is the $ERROR block code we used for a simple proportional RV model
>
> $ERROR (ONLY OBSERVATIONS)
>
> ;Information needed for BLQF and > BLQF samples
> LOQ=5
> SIG = THETA(3)
>
> DFLG=0 ;create a dose record flag
> IF(AMT.NE.0) DFLG=1
>
> IPRED=F+DFLG
> W=IPRED
>
> ;Computations for samples with DV > LOQ (BLQF=0)
> IF (BLQF.LT.0.25) THEN
> F_FLAG=0
> FFLG=0
> IRES=DV-IPRED
> IWRES=IRES/W
> Y=IPRED+W*SIG*EPS(1) ;NOTE: Prediction is a concentration
> ENDIF
>
> ;Computations for samples with DV <= LOQ (BLQF=1)
> IF (BLQF.GE.0.25) THEN
> F_FLAG=1
> FFLG=1
> DUM=(LOQ-IPRED)/(W*SIG)
> IPRED=PHI(DUM)
> Y=IPRED ;NOTE: prediction is a *probability*
> ENDIF