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RE: RES and WRES output with Beal's M3 method

From: Matt Hutmacher <matt.hutmacher>
Date: Wed, 7 Apr 2010 15:39:52 -0400

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


-----Original Message-----
From: owner-nmusers
Behalf Of Martin Bergstrand
Sent: Wednesday, April 07, 2010 1:02 PM
To: 'Sebastien Bihorel'; nmusers
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
-----------------------------------------------
martin.bergstrand
-----------------------------------------------
Work: +46 18 471 4639
Mobile: +46 709 994 396
Fax: +46 18 471 4003


-----Original Message-----
From: owner-nmusers
Behalf Of Sebastien Bihorel
Sent: den 7 april 2010 17:31
To: nmusers
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

Received on Wed Apr 07 2010 - 15:39:52 EDT

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