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Re: [NMusers] Splitting the residual error

From: Ekaterina Gibiansky <egibiansky_at_quantpharm.com>
Date: Fri, 13 May 2016 18:13:06 -0400

Hi Steve and Andre,

No, nothing fancy, I did not even estimate the time of the switch.
Something like this:

ITAD=1
IF(TAD.LE.4) ITAD=THETA(10)
Y=F + (F*ITAD*ERR(1)+ERR(2))

ITAD can also include differences in populations, e.g. Phase 1 versus
Phase 2-3 data, or healthy versus patients, etc.

The number for switch usually is pretty obvious from the data, but yes
estimating it would be nicer.

Regards,
Katya

On 5/13/2016 4:33 PM, Stephen Duffull wrote:
>
> Hi Katya
>
> Are they discrete models with estimated transition times (e.g. error
> model 1 from 0 to 2 hours, then error model 2 etc) or continuous
> models (two models who’s weighting is time dependent) that overlap?
>
> We do the former as a diagnostic but not the latter at this point. But
> I have pondered.
>
> Cheers
>
> Steve
>
> *From:*owner-nmusers_at_globomaxnm.com
> [mailto:owner-nmusers_at_globomaxnm.com] *On Behalf Of *Ekaterina Gibiansky
> *Sent:* Saturday, 14 May 2016 2:07 a.m.
> *To:* Jonathan Moss <jjmoss_at_btconnect.com>; nmusers_at_globomaxnm.com
> *Subject:* Re: [NMusers] Splitting the residual error
>
> Dear Jon,
>
> We routinely use separate residual errors during absorption and later
> (say, a larger error in the first X hours), although I do not remember
> whether we published any of those models.
>
> Regards,
> Katya
>
> Ekaterina Gibiansky, Ph.D.
> CEO&CSO, QuantPharm LLC
> Web:www.quantpharm.com
> Email:EGibiansky_at_quantpharm.com <mailto:Email:EGibiansky_at_quantpharm.com>
>
> On 5/13/2016 6:37 AM, Mats Karlsson wrote:
>
> Dear Jon,
>
> As you point out the concept of residual error magnitude being
> dependent on anything else than the prediction itself is a
> straightforward. Yet it is, I think underused and that is why you
> may not see it much in the literature. In addition to what you
> mention, a large component is that model misspecification is not a
> homogeneous process. It is likely that most of our models are more
> specified for absorption than disposition. Absorption contains
> many processes that are discrete and difficult to easily capture
> in simple models.
>
> For most compunds, the absolute gradient is much higher during the
> absorption phase than the distribution phase and that is probably
> a contributing factor to what experience. You would probably get
> as good an improvement if you had a separate error magnitude
> during the absorption phase.
>
> The model you mentioned with were outlined in the 1998 article
> below. I also add some other articles for the case you’re further
> interested in residual error modeling.
>
> Best regards,
>
> Mats
>
> 1.
>
>
>
> A strategy for residual error modeling incorporating scedasticity
> of variance and distribution shape.
> <http://www.ncbi.nlm.nih.gov/pubmed/26679003>
>
>
>
>
> Dosne AG, Bergstrand M, Karlsson MO.
>
>
>
>
> J Pharmacokinet Pharmacodyn. 2016 Apr;43(2):137-51. doi:
> 10.1007/s10928-015-9460-y. Epub 2015 Dec 17.
>
>
>
>
> PMID: 26679003 [PubMed - in process]*Free PMC Article *
>
>
>
>
> Similar articles
> <http://www.ncbi.nlm.nih.gov/pubmed?linkname=pubmed_pubmed&from_uid=26679003>
>
>
> 2.
>
>
>
> The impact of misspecification of residual error or correlation
> structure on the type I error rate for covariate inclusion.
> <http://www.ncbi.nlm.nih.gov/pubmed/19219538>
>
>
>
>
> Silber HE, Kjellsson MC, Karlsson MO.
>
>
>
>
> J Pharmacokinet Pharmacodyn. 2009 Feb;36(1):81-99. doi:
> 10.1007/s10928-009-9112-1. Epub 2009 Feb 14.
>
>
>
>
> PMID: 19219538 [PubMed - indexed for MEDLINE]
>
>
>
>
> Similar articles
> <http://www.ncbi.nlm.nih.gov/pubmed?linkname=pubmed_pubmed&from_uid=19219538>
>
>
> 3.
>
>
>
> Three new residual error models for population PK/PD analyses.
> <http://www.ncbi.nlm.nih.gov/pubmed/8733951>
>
>
>
>
> Karlsson MO, Beal SL, Sheiner LB.
>
>
>
>
> J Pharmacokinet Biopharm. 1995 Dec;23(6):651-72.
>
>
>
>
> PMID: 8733951 [PubMed - indexed for MEDLINE]
>
>
>
>
> Similar articles
> <http://www.ncbi.nlm.nih.gov/pubmed?linkname=pubmed_pubmed&from_uid=8733951>
>
>
> 4.
>
>
>
> Assumption testing in population pharmacokinetic models:
> illustrated with an analysis of moxonidine data from congestive
> heart failure patients. <http://www.ncbi.nlm.nih.gov/pubmed/9795882>
>
>
>
>
> *Karlsson MO*, Jonsson EN, Wiltse CG, Wade JR.
>
>
>
>
> J Pharmacokinet Biopharm. 1998 Apr;26(2):207-46.
>
>
>
>
> PMID:
>
>
>
>
> 9795882
>
> Mats Karlsson, PhD
>
> Professor of Pharmacometrics
>
> Dept of Pharmaceutical Biosciences
>
> Faculty of Pharmacy
>
> Uppsala University
>
> Box 591
>
> 75124 Uppsala
>
> Phone: +46 18 4714105
>
> Fax + 46 18 4714003
>
> www.farmbio.uu.se/research/researchgroups/pharmacometrics/
> <http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/>
>
> *From:*owner-nmusers_at_globomaxnm.com
> <mailto:owner-nmusers_at_globomaxnm.com>
> [mailto:owner-nmusers_at_globomaxnm.com] *On Behalf Of *Jonathan Moss
> *Sent:* Friday, May 13, 2016 11:37 AM
> *To:* nmusers_at_globomaxnm.com <mailto:nmusers_at_globomaxnm.com>
> *Subject:* [NMusers] Splitting the residual error
>
> Dear all,
>
> I would like to share with you and get people’s opinions on a
> recent issue I had.
>
> I have a data set of 46 patients, orally dosed, with very dense
> sampling during absorption (0.25h, 0.5h, 0.75h, 1h, 1.5h, 2h, 3h,
> 4h, 6h, 8h, 12h, 24h, 36h), Cmax at around 4 hours.
>
> During modelling, I found that the residual error was not evenly
> distributed. Plotting CWRES against time after dose, the result
> looked like an “hourglass” shape. I.e. A wide spread during
> absorption, narrower near Cmax time, then wider at later time points.
>
> My thinking was as follows: Residual error contains both the assay
> / model spec. error, and the error in recorded observation time.
> When the gradient of the PK curve is large, any error in recorded
> observation time equals a large error in the recorded
> concentration, whereas if the gradient is small then the recorded
> concentration error will be small.
>
> I “split” the residual error into its assay/model spec and
> time-error parts in the $ERROR block:
>
> $ERROR
>
> GRAD = KA*A(2) - K20*A(3)
>
> IF (GRAD.LT.0) GRAD = -1*GRAD
>
> C_1 = A(3)/V ; Concentration
> in the central compartment
>
> IPRED = C_1
>
> SD = SQRT(EPROP*C_1**2) ; Standard
> deviation of predicted concentration
>
> Y=IPRED+SD*(1+val*GRAD)*EPS(1)
>
> Note: Sigma is fixed to one and EPROP is estimated as a theta.
> Here, GRAD is the right hand side of the differential equation for
> A(3), in order to recover the gradient. Val is estimated by NONMEM.
>
> This approach vastly improved the model fit (OFV drop of around
> 350!). All GOF plots, VPCs, NPCs, NPDEs, individual fits looked
> good. This got me thinking, and I tried this approach on some of
> my other popPK models. I found for the simpler models, the result
> was nearly always a significant improvement in the model fit. For
> the more complicated models, NONMEM had trouble finishing the runs.
>
> I struggled to find any approach like this in the literature,
> which leads me to believe that there is something wrong, as it is
> a relatively simple concept. Please, what are peoples thoughts on
> this?
>
> Thanks,
>
> Jon
>
> *Jon Moss, PhD*
>
> Modeller
>
> *BAST Inc Limited*
>
> Loughborough Innovation Centre
>
> Charnwood Wing
>
> Holywell Park
>
> Ashby Road
>
> Loughborough, LE11 3AQ, UK
>
> Tel: +44 (0)1509 222908
>



Received on Fri May 13 2016 - 18:13:06 EDT

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