NONMEM Users Network Archive

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Re: M3 method - WRES, and CWRES

From: Jeroen Elassaiss-Schaap <jeroen>
Date: Wed, 2 Sep 2020 08:58:11 +0200

Hi Mutaz, Bill,

It might be useful to use NPDEs, as discussed in
https://www.cognigen.com/nmusers/2019-February/7376.html; the whole
thread is worthwhile reading. NPDEs can be calculated also for BQL values.

Bill -thanks for pointing to excellent post of Matt! I would take as
most important point that CWRES for non-BQL values, calculated with a
model with influential BQL, are biased because the influence of the BQL
values is not accounted for. (if a certain prediction for a measurable
concentration is changed by 10% because of the M3 method, that will turn
up as a similar bias in CWRES). The NPDEs as referenced to in the above
discussion (Nguyen2012 JPKPD 0.1007/s10928-012-9264-2) do not suffer
from that drawback as one can see the complete profile (cf Fig 8 of
Nguyen2012).

Hope this helps,

Jeroen

http://pd-value.com
jeroen
  
+31 6 23118438
-- More value out of your data!

On 2/9/20 2:32 am, Bill Denney wrote:
>
> Hi Mutaz,
>
> Matt Hutmacher described it well here:
> https://www.cognigen.com/nmusers/2010-April/2448.html
>
> A very brief summary of his excellent post is that subjects with a
> combination of censored (BLQ) an uncensored (above the LLOQ and below
> the ULOQ) will be biased in their reporting of CWRES because you
> cannot calculate CWRES for BLQ values.  (I say this before looking up
> what MDVRES does.)
>
> My guess that Bob or someone else can confirm is that the bias is
> anticipated to be relatively small compared to the value of being able
> to compare CWRES values the other observations for a subject.  It does
> not definitively mean that the results are unbiased (see Matt’s Tmax
> example), but generally, the CWRES values previously omitted are more
> useful than excluding them from calculation.
>
> Thanks,
>
> Bill
>
> *From:* owner-nmusers
> <mailto:owner-nmusers
> <mailto:owner-nmusers
> *Sent:* Tuesday, September 1, 2020 7:25 PM
> *To:* nmusers
> *Subject:* [NMusers] M3 method - WRES, and CWRES
>
> All,
>
> Back in April 2010, Sebastian Bihorel and Martin Bergstrand initiated
> a discussion regarding using the M3 and M4 methods for handling BQL
> data and how it seemed to be a bug that NONMEM wouldn't compute WRES
> for the entire set of subject data records whenever a BQL was included
> (https://www.cognigen.com/nmusers/2010-April/2445.html). Tom Ludden
> responded with the following post
> (https://www.cognigen.com/nmusers/2010-April/2447.html):
>
> 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.
>
> Since then, we now have easy functionality with the F_FLAG=1 condition
> of the M3/M4 code in $ERROR to tack on MDVRES=1 that allows the
> calculation of WRES and CWRES to be available in output tables.
>
> My questions are: Is Stuart Beal's original concern still valid?  Do
> these NONMEM updates give us appropriate WRES and CWRES for plotting
> purposes for individuals whose records contain BQL data?
>
> Thank you,
>
> Mutaz Jaber
>
> PhD student
>
> University of Minnesota
>
> -------------------------------------------------------
>
> *Mutaz M. Jaber, PharmD.*
>
> PhD student, Pharmacometrics
>
> Experimental and Clinical Pharmacology
>
> University of Minnesota
>
> 717 Delaware St SE; Room 468
>
> Minneapolis, MN 55414
>
> Email: jaber038
>
> Phone: +1 651-706-5202
>
> *~ Stay curious*
>

Received on Wed Sep 02 2020 - 02:58:11 EDT

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