Hi Jeroen,

Jumping in a bit later, I agree generally with what has been said so far, but I 
do disagree with one point.  I think that the models we work with tend to have 
local minima that cause us to find different "best models" depending on the 
path taken to get there.

And, I brush after breakfast to preserve the taste of the meal.

Thanks,

Bill

On Nov 23, 2010, at 4:49 PM, "Elassaiss - Schaap, J. (Jeroen)" 
<jeroen.elassa...@spcorp.com> wrote:

> Hi Paolo,
> 
> It is a bit late to chime in but I can't resist... Great discussion point! I 
> am of the opinion that if we develop models robustly, it in the end should 
> not matter. If we would introduce a structural bias by neglecting BOV early 
> on, we should be able to see a reflection of that in a diagnostic plot after 
> introduction of BOV. And that in turn should lead to evaluation of other 
> structural models. But this obviously depends on close scrutiny of 
> diagnostics and frequent back-tracing.
> 
> Perhaps the question could be restated as: which method is more efficient?  - 
> retaining the original answer. 
> 
> It may even be generalized by stating that those model parts that describe 
> most of variance in the most plausible manner should be introduced first. 
> This should prevent bias that complicates evaluation of more detailed parts 
> because of nonlinearity issues as you described. 
> 
> Such a rule could be applied to any model and result in e.g. BSV on baseline 
> be added early on for a PK-PD problem, body weight for general PK, BOV for 
> multi-occasion/rich sampling problems, to name a few. 
> 
> Last but not least, I skip breakfast completely ;-). 
> 
> Best regards,
> Jeroen
> 
> Modeling & Simulation Expert
> Pharmacokinetics, Pharmacodynamics & Pharmacometrics (P3) - DMPK
> MSD
> PO Box 20 - AP1112
> 5340 BH Oss
> The Netherlands
> jeroen.elassa...@merck.com
> T: +31 (0)412 66 9320
> M: +31 (0)6 46 101 283
> F: +31 (0)412 66 2506
> www.msd.com
> 
> 
> 
> -----Original Message-----
> From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
> Behalf Of Paolo Denti
> Sent: Wednesday, 17 November, 2010 17:23
> To: Elodie Plan
> Cc: 'nmusers'
> Subject: Re: [NMusers] Zähneputzen VOR oder NACH dem Frühstück? What comes 
> first? BSV, BOV, or covariates?
> 
> Thank you Elodie,
> the reference you mention also states that the covariates were tested only on 
> parameters for which BOV and BSV were significant. This is generally the 
> approach I use, so that I can test whether the mentioned variabilities are 
> indeed explained with the inclusion of covariates. I wonder if somebody can 
> think of any exceptions to this "rule"?
> 
> Also, both Oscar della Pasqua and Coen Van Hasselt pointed to me this PAGE 
> poster (unfortunately presented in a literally burning hot poster session in 
> Berlin):
> http://www.page-meeting.org/default.asp?abstract=1887
> which seems to stress that disregarding BOV might lead to model 
> misspecification.
> 
> I also got a reply from Alwin Huitema, who told me that his experience with 
> modelling in HIV is that ignoring IOV early in the modelling process might 
> guide to wrong models.
> 
> Any supporters of an alternative approach or shall I just assume that I was 
> doing the same as everybody else?
> 
> Who would brush teeth before breakfast anyway? ;) Another, safer, option is 
> suggested by Oscar:
>> Paolo,
>> 
>> By the way, hygiene rules do suggest you brush your teeth before and after 
>> breakfast.
>> I don't want to infer that this is the same for modelling but I can 
>> say that you can recognise the individual ingredients in your 
>> breakfast if your taste butts are clean:)
>> 
>> Oscar
> Ciao,
> Paolo
> 
> 
> 
> On 16/11/2010 22:15, Elodie Plan wrote:
>> Dear Paolo,
>> 
>> Thanks for this interesting NMusers thread.
>> 
>> I think the order you are describing really makes sense in theory, for 
>> the reasons you describe, but in brief because it seems covariates 
>> should be incorporated on a model already fully developed structurally 
>> and statistically, so this includes IOV. Moreover, the covariates will 
>> increase the predictive performance (and the understanding) of the 
>> model, by being introduced on structural parameters, but also possibly 
>> directly on IIV and IOV.
>> 
>> I also wanted to verify that this was what was done in practice, there 
>> were
>> 6 entries when searching for "occasion AND covariate AND NONMEM" on 
>> PubMed, I can recommend the following where the decrease in 
>> variability magnitude following the covariate model building is nicely 
>> discussed: Sandström M, Lindman H, Nygren P, Johansson M, Bergh J, 
>> Karlsson MO. Population analysis of the pharmacokinetics and the 
>> haematological toxicity of the fluorouracil-epirubicin-cyclophosphamide 
>> regimen in breast cancer patients.
>> Cancer Chemother Pharmacol. 2006 Aug;58(2):143-56.
>> 
>> Best regards,
>> Elodie
>> 
>> PS: IOV or breakfast, I like it first :)
>> 
>> Elodie L. Plan, PharmD, MSc, PhD student
>> ********************************************
>> Uppsala Pharmacometrics Research Group Department of Pharmaceutical 
>> Biosciences P.O. Box 591, SE-751 24 Uppsala, SWEDEN Mob +46 76-242 
>> 1256, Skype "ppeloo"
>> 
>> -----Original Message-----
>> From:owner-nmus...@globomaxnm.com  
>> [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Paolo Denti
>> Sent: Tuesday, November 16, 2010 10:10 AM
>> To: nmusers
>> Subject: [NMusers] Zähneputzen VOR oder NACH dem Frühstück? What comes 
>> first? BSV, BOV, or covariates?
>> 
>> Dear all,
>> don't be discouraged by the subject, this is indeed NMUsers and not 
>> German 101, and this post is about pharmacometrics, please read on... 
>> ;)
>> 
>> The subject of the message comes from when I was studying German, and 
>> from an exercise in our book with lots of colourful pictures. The 
>> point of the exercise was only to teach us how to say "tooth 
>> brushing", "have breakfast", "before" and "after", but instead it 
>> sprouted a lively discussion in the class about what comes first and 
>> last in everybody's morning routine... So I thought it would be an 
>> appropriate title for this post, which is a survey/question about what 
>> modelling approach people use/recommend for model development.
>> 
>> Just to contextualize a bit, here at UCT we mainly study HIV and TB 
>> drugs, which are dosed repeatedly (once or twice per day) and administered 
>> orally.
>> We often have data available on more than one sampling occasion, and 
>> many times these occasions are virtually
>> equivalent: no changes in co-treatment or other covariates, just a 
>> mere repetition of the experiment on a different day. Confirming what 
>> Mats recently pointed out in a post about the use of BOV, our 
>> experience is that, especially in the absorption phase, the 
>> contribution of BOV is dominant, and cannot be ignored. The absorption 
>> is often subject to random delays and factors that are mostly 
>> occasion-specific and not measurable/available in the dataset.
>> 
>> Therefore, when I start modelling new data, I normally proceed as follows:
>> 1. I initially assume every occasion as a separate profile, either 
>> using dummy IDs (and pretending it's different subjects) or coding all 
>> variability as BOV. I believe this allows the maximum flexibility to 
>> test the structural model, and I find that, if I don't proceed like 
>> this, I may run into troubles detecting the correct structural model. 
>> In this early stage of model development, I mostly use individual 
>> plots, and try to see if my prediction profile is flexible enough to run 
>> through the points.
>> 
>> 2. Then I try to see if some of the variability is subject-specific 
>> (normally V and CL) and can be better explained either by only BSV or 
>> both BSV and BOV. I use the OFV to guide this process, but if the BOV 
>> is much larger than BSV, and physiology supports the hypothesis that 
>> the parameter be occasion-specific, I tend to disregard BSV.
>> 
>> 3. Once I believe I got my structural model right, and organized the 
>> hierarchy of random variability in a decent way, I start incorporating 
>> the covariates. If they turn out to be significant, I see that BOV and 
>> BSV decrease, and sometimes become superfluous in the model and can be 
>> removed.
>> 
>> I know other modellers would recommend first introducing BSV and/or 
>> covariates, before considering BOV and I would be interested in 
>> knowing people's opinion about this. Each method probably has its pros 
>> and cons, and I would really value your input about this topic. What 
>> are the advantages and disadvantages of the different approaches?
>> 
>> Since I favour the modus operandi I just explained, I give my reasons, 
>> and look forward to some comments. My opinion (but I am  obviously
>> biased) is that it does not hurt to include BOV first, since it is 
>> easy to remove from the model if the same variability is explained by 
>> covariates, and likely, if this is the case, BOV will decrease in size.
>> On the other hand, disregarding BOV might prevent the identification 
>> of the correct structural model. I am thinking, for example, about a 
>> comparison between 2-cmpt vs 1-cmpt when the absorption is subject to 
>> substantial random delays. If BOV is not considered, this is 
>> equivalent to pooling the data from all occasions, with the potential 
>> result of having a cloud of points without much structure... And also, 
>> as a general rule, I would allow a parameter to move with an ETA, 
>> before I try to explain its changes with a covariate effect. In this 
>> way I can also test better if the covariate is explaining some of this 
>> variability.
>> 
>> Ok, I've been once again way too lengthy, apologies. Any comments/thoughts?
>> In other words, do you first brush your teeth or have breakfast? 
>> Please join the survey! ;)
>> 
>> Greetings from Cape Town,
>> Paolo
>> 
>> 
>> PS Ich putze die Zähne immer NACH dem Frühstück... I can't enjoy 
>> coffee with that minty toothpaste after-taste... :)
>> 
>> --
>> ------------------------------------------------
>> Paolo Denti, PhD
>> Post-Doctoral Fellow
>> Division of Clinical Pharmacology
>> Department of Medicine
>> University of Cape Town
>> 
>> K45 Old Main Building
>> Groote Schuur Hospital
>> Observatory, Cape Town
>> 7925 South Africa
>> phone: +27 21 404 7719
>> fax: +27 21 448 1989
>> email:paolo.de...@uct.ac.za
>> ------------------------------------------------
>> 
>> 
>> 
>> 
>> 
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> 
> --
> ------------------------------------------------
> Paolo Denti, PhD
> Post-Doctoral Fellow
> Division of Clinical Pharmacology
> Department of Medicine
> University of Cape Town
> 
> K45 Old Main Building
> Groote Schuur Hospital
> Observatory, Cape Town
> 7925 South Africa
> phone: +27 21 404 7719
> fax: +27 21 448 1989
> email:paolo.de...@uct.ac.za
> ------------------------------------------------
> 
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