Re: M3 method - WRES, and CWRES
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
[email protected]
@PD_value
+31 6 23118438
-- More value out of your data!
Quoted reply history
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:* [email protected] < mailto: [email protected] > < [email protected] < mailto: [email protected] >> *On Behalf Of *Mu'taz Jaber
>
> *Sent:* Tuesday, September 1, 2020 7:25 PM
> *To:* [email protected] <mailto:[email protected]>
> *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: [email protected] <mailto:[email protected]>
>
> Phone: +1 651-706-5202
>
> *~ Stay curious*