Re: [External] Re: [NMusers] M3 method - WRES, and CWRES
I have a follow-up question on CWRES with M4 method. I was able to run my model with M3 method, I got NPDE and CWRES (with MDVRES=1) calculated just fine. Then I changed to M4 method by adding YLO to the non-BLQ data, the model stilled converged but in the output table, some of the subjects had CWRES = 0 and NPDE was a constant (around 3). The problem with CWRES and NPDE is not specific to subjects with BLQ observations, but rather, it followed a pattern like this: CWRES and NPDE was calculated for subjects number 1, 3, 5, 7, etc. and not calculated for subjects 2, 4, 6, 8, etc. I suspected that YLO option was the cause, so I ran the model with M2 method. Indeed, all CWRES was 0. This might not be a new problem, but I searched through the NMusers archives and most discussions focused on M3 only. Is there any reason why there is less focus on M2 and M4? Thank you From: owner-nmus...@globomaxnm.com on behalf of Matthew Fidler Sent: Saturday, September 5, 2020 10:17 AM To: Bauer, Robert Cc: nmusers@globomaxnm.com Subject: [External] Re: [NMusers] M3 method - WRES, and CWRES Thank you Bob, The NPDE 2.0 manual discusses the methods that NPDE uses to handle BLQ, including replacing values with pred, ipred, or lloq, or simulating from a uniform random value while calculating the NPDE (cdf method). The NONMEM manual doesn't mention the method used. My guess is the cdf method. I realize that no one has answered Mu'taz's question. As far as if the CWRES is appropriate for BLQ data, the CWRES method uses the FOCEi approximation to calculate residuals. However with M3/M4 and other methods the likelihood for these points is not the FOCEi objective function but the M3/M4 likelihood so anything you do here with CWRES doesn't follow or add to the likelihood observed during minimization. Therefore in my opinion, there will be bias of some sort here. Best Regards, Matt. On Thu, Sep 3, 2020 at 3:06 PM Bauer, Robert mailto:robert.ba...@iconplc.com>> wrote: Matt: The NPDE and NPD systems in NONMEM are described in the nm744.pdf manual ( https://nonmem.iconplc.com/nonmem744 ), pages 70-75, and follow along the work of Comet, Brendel, Ngyuen, Mentre, etc. The NPDE R package is not used within NONMEM. Robert J. Bauer, Ph.D. Senior Director Pharmacometrics R ICON Early Phase 820 W. Diamond Avenue Suite 100 Gaithersburg, MD 20878 Office: (215) 616-6428 Mobile: (925) 286-0769 robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com> www.iconplc.com<http://www.iconplc.com> From: owner-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> mailto:owner-nmus...@globomaxnm.com>> On Behalf Of Matthew Fidler Sent: Thursday, September 3, 2020 6:08 AM To: Jeroen Elassaiss-Schaap (PD-value B.V.) mailto:jer...@pd-value.com>> Cc: Bill Denney mailto:wden...@humanpredictions.com>>; Mu'taz Jaber mailto:jaber...@umn.edu>>; nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com> Subject: Re: [NMusers] M3 method - WRES, and CWRES Hi everyone, As an aside, nlmixr's upcoming release (that supports censoring) simulates a value using a truncated normal based on the ipred, variance at that point and the censoring column to produce an observation. This observation is used to calculate RES, WRES, CWRES. It is flagged so you can see which values use this approach. In theory, since this is simulated from the IPRED/truncated the CWRES would be likely follow the distribution closer. I'm unsure if the new NONMEM uses this approach. Another question from my end is the NPDE: There are many methods to handle BLQ values with NPDE R package, does anyone know which NONMEM uses? Or do you need to use the NPDE package to get these values from NONMEM? Matt. On Wed, Sep 2, 2020 at 2:09 AM Jeroen Elassaiss-Schaap (PD-value B.V.) mailto:jer...@pd-value.com>> wrote: 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 jer...@pd-value.com<mailto:jer...@pd-value.com> @PD_value +31 6 23118438 -- More value out of your data! On 2/9/20 2:32 am, Bill Denney wrote: Hi Mutaz, Matt Hutmac
Re: [NMusers] M3 method - WRES, and CWRES
Thank you Bob, The NPDE 2.0 manual discusses the methods that NPDE uses to handle BLQ, including replacing values with pred, ipred, or lloq, or simulating from a uniform random value while calculating the NPDE (cdf method). The NONMEM manual doesn't mention the method used. My guess is the cdf method. I realize that no one has answered Mu'taz's question. As far as if the CWRES is appropriate for BLQ data, the CWRES method uses the FOCEi approximation to calculate residuals. However with M3/M4 and other methods the likelihood for these points is not the FOCEi objective function but the M3/M4 likelihood so anything you do here with CWRES doesn't follow or add to the likelihood observed during minimization. Therefore in my opinion, there will be bias of some sort here. Best Regards, Matt. On Thu, Sep 3, 2020 at 3:06 PM Bauer, Robert wrote: > Matt: > > The NPDE and NPD systems in NONMEM are described in the nm744.pdf manual ( > https://nonmem.iconplc.com/nonmem744 ), pages 70-75, and follow along the > work of Comet, Brendel, Ngyuen, Mentre, etc. The NPDE R package is not > used within NONMEM. > > > > > > Robert J. Bauer, Ph.D. > > Senior Director > > Pharmacometrics R > > ICON Early Phase > > 820 W. Diamond Avenue > > Suite 100 > > Gaithersburg, MD 20878 > > Office: (215) 616-6428 > > Mobile: (925) 286-0769 > > robert.ba...@iconplc.com > > www.iconplc.com > > > > *From:* owner-nmus...@globomaxnm.com *On > Behalf Of *Matthew Fidler > *Sent:* Thursday, September 3, 2020 6:08 AM > *To:* Jeroen Elassaiss-Schaap (PD-value B.V.) > *Cc:* Bill Denney ; Mu'taz Jaber < > jaber...@umn.edu>; nmusers@globomaxnm.com > *Subject:* Re: [NMusers] M3 method - WRES, and CWRES > > > > Hi everyone, > > > > As an aside, nlmixr's upcoming release (that supports censoring) simulates > a value using a truncated normal based on the ipred, variance at that point > and the censoring column to produce an observation. This observation is > used to calculate RES, WRES, CWRES. It is flagged so you can see which > values use this approach. In theory, since this is simulated from the > IPRED/truncated the CWRES would be likely follow the distribution closer. > > > > I'm unsure if the new NONMEM uses this approach. > > > > Another question from my end is the NPDE: There are many methods to > handle BLQ values with NPDE R package, does anyone know which NONMEM uses? > Or do you need to use the NPDE package to get these values from NONMEM? > > > > Matt. > > > > > > > > On Wed, Sep 2, 2020 at 2:09 AM Jeroen Elassaiss-Schaap (PD-value B.V.) < > jer...@pd-value.com> wrote: > > 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 > > jer...@pd-value.com > > @PD_value > > +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-nmus...@globomaxnm.com *On > Behalf Of *Mu't
RE: [NMusers] M3 method - WRES, and CWRES
Matt: The NPDE and NPD systems in NONMEM are described in the nm744.pdf manual ( https://nonmem.iconplc.com/nonmem744 ), pages 70-75, and follow along the work of Comet, Brendel, Ngyuen, Mentre, etc. The NPDE R package is not used within NONMEM. Robert J. Bauer, Ph.D. Senior Director Pharmacometrics R ICON Early Phase 820 W. Diamond Avenue Suite 100 Gaithersburg, MD 20878 Office: (215) 616-6428 Mobile: (925) 286-0769 robert.ba...@iconplc.com<mailto:robert.ba...@iconplc.com> www.iconplc.com<http://www.iconplc.com> From: owner-nmus...@globomaxnm.com On Behalf Of Matthew Fidler Sent: Thursday, September 3, 2020 6:08 AM To: Jeroen Elassaiss-Schaap (PD-value B.V.) Cc: Bill Denney ; Mu'taz Jaber ; nmusers@globomaxnm.com Subject: Re: [NMusers] M3 method - WRES, and CWRES Hi everyone, As an aside, nlmixr's upcoming release (that supports censoring) simulates a value using a truncated normal based on the ipred, variance at that point and the censoring column to produce an observation. This observation is used to calculate RES, WRES, CWRES. It is flagged so you can see which values use this approach. In theory, since this is simulated from the IPRED/truncated the CWRES would be likely follow the distribution closer. I'm unsure if the new NONMEM uses this approach. Another question from my end is the NPDE: There are many methods to handle BLQ values with NPDE R package, does anyone know which NONMEM uses? Or do you need to use the NPDE package to get these values from NONMEM? Matt. On Wed, Sep 2, 2020 at 2:09 AM Jeroen Elassaiss-Schaap (PD-value B.V.) mailto:jer...@pd-value.com>> wrote: Hi Mutaz, Bill, It might be useful to use NPDEs, as discussed in https://www.cognigen.com/nmusers/2019-February/7376.html<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<http://pd-value.com> jer...@pd-value.com<mailto:jer...@pd-value.com> @PD_value +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<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-nmus...@globomaxnm.com<mailto:owner-nmus...@globomaxnm.com> mailto:owner-nmus...@globomaxnm.com>> On Behalf Of Mu'taz Jaber Sent: Tuesday, September 1, 2020 7:25 PM To: nmusers@globomaxnm.com<mailto:nmusers@globomaxnm.com> 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<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<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
Re: [NMusers] M3 method - WRES, and CWRES
Hi everyone, As an aside, nlmixr's upcoming release (that supports censoring) simulates a value using a truncated normal based on the ipred, variance at that point and the censoring column to produce an observation. This observation is used to calculate RES, WRES, CWRES. It is flagged so you can see which values use this approach. In theory, since this is simulated from the IPRED/truncated the CWRES would be likely follow the distribution closer. I'm unsure if the new NONMEM uses this approach. Another question from my end is the NPDE: There are many methods to handle BLQ values with NPDE R package, does anyone know which NONMEM uses? Or do you need to use the NPDE package to get these values from NONMEM? Matt. On Wed, Sep 2, 2020 at 2:09 AM Jeroen Elassaiss-Schaap (PD-value B.V.) < jer...@pd-value.com> wrote: > 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.comjer...@pd-value.com > @PD_value > +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-nmus...@globomaxnm.com *On > Behalf Of *Mu'taz Jaber > *Sent:* Tuesday, September 1, 2020 7:25 PM > *To:* nmusers@globomaxnm.com > *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: jaber...@umn.edu > > Phone: +1 651-706-5202 > > > > *~ Stay curious* > >
Re: [NMusers] 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 jer...@pd-value.com @PD_value +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-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com> <mailto:owner-nmus...@globomaxnm.com>> *On Behalf Of *Mu'taz Jaber *Sent:* Tuesday, September 1, 2020 7:25 PM *To:* nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> *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: jaber...@umn.edu <mailto:jaber...@umn.edu> Phone: +1 651-706-5202 *~ Stay curious*
RE: [NMusers] M3 method - WRES, and CWRES
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-nmus...@globomaxnm.com *On Behalf Of *Mu'taz Jaber *Sent:* Tuesday, September 1, 2020 7:25 PM *To:* nmusers@globomaxnm.com *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: jaber...@umn.edu Phone: +1 651-706-5202 *~ Stay curious*
[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: jaber...@umn.edu Phone: +1 651-706-5202 *~ Stay curious*
