Mark

I disagree (more than a little).  If a sample is reported as BQL and the 
expected value is 0 (i.e., pre-dose, not endogenous), what information is there 
about assay precision.  If the error model is additive (or additive + 
proportional), the sample will contribute zero to the objective function.  If 
the error model is proportional, NONMEM will report an error (0/0).  I agree 
that pre-dose samples > LOQ provide IMPORTANT information about assay precision.

Were you evacuated during the fires?

Dennis

Dennis Fisher MD
P < (The "P Less Than" Company)
Phone / Fax: 1-866-PLessThan (1-866-753-7784)
www.PLessThan.com <http://www.plessthan.com/>




> On Nov 6, 2019, at 8:40 AM, Mark Sale <ms...@nuventra.com> wrote:
> 
> Dennis,
> I may have to disagree, a little. There is information, a little, in a pre 
> dose BQL, about assay precision. I think you might agree that is a pre dose 
> sample is NOT BQL (which happens), tells you (and NONMEM) something about the 
> assay. Converse, even a BQL predose sample has a small amount of information.
> That not withstanding, we also remove and pre-dose BQLs from the data set.
> 
> 
> Mark Sale M.D.
> Senior Vice President, Pharmacometrics
> Nuventra Inc.
> 2525 Meridian Parkway, Suite 200
> Durham, NC 27713
> Phone (919)-973-0383
> ms...@nuventra.com <x-msg://138/ms...@kinetigen.com> 
>  
>  
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> From: owner-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com> 
> <owner-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com>> on 
> behalf of Dennis Fisher <fis...@plessthan.com <mailto:fis...@plessthan.com>>
> Sent: Wednesday, November 6, 2019 7:23 AM
> To: nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com> 
> <nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com>>; Bill Denney 
> <wden...@humanpredictions.com <mailto:wden...@humanpredictions.com>>
> Cc: Carlos ST <carlos.serr...@gmail.com <mailto:carlos.serr...@gmail.com>>; 
> Steven Shafer <steven.sha...@stanford.edu <mailto:steven.sha...@stanford.edu>>
> Subject: Re: [NMusers] Using evid 0 before dosing
>  
> WARNING: This email originated from outside of the company. Do not click 
> links or open attachments unless you recognize the sender and are expecting 
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> Bill
> 
> I think that the issue is more complicated than you acknowledge.  
> 
> Assuming that the drug is not an endogenous substance, the pre-dose 
> concentration is likely to be BQL.  However, there are two kinds of BQL 
> values:
> 1.  Samples that are truly zero because they were obtained pre-dose
> 2.  Samples that are > 0 but < LOQ.
> Yet both are reported as BQL.
> 
> From my perspective, a BQL value pre-dose provides no information to NONMEM.  
> Therefore, one should apply EVID=2 to that sample.  This allows a prediction 
> at that timepoint but the sample does not influence the analysis.
> 
> Dennis
> 
> Dennis Fisher MD
> P < (The "P Less Than" Company)
> Phone / Fax: 1-866-PLessThan (1-866-753-7784)
> www.PLessThan.com <http://www.plessthan.com/>
> 
> 
> 
> 
>> On Nov 6, 2019, at 7:12 AM, Bill Denney <wden...@humanpredictions.com 
>> <mailto:wden...@humanpredictions.com>> wrote:
>> 
>> Hi Carlos,
>> 
>> It is commonly used.  For most datasets, there will be at least one
>> observation that occurs before dosing to estimate the baseline value, and in
>> almost every scenario, the modeling dataset should mirror the real world
>> actions.  So, there is no issue with it, and usually you will have an EVID=0
>> before the first dose.
>> 
>> Thanks,
>> 
>> Bill
>> 
>> -----Original Message-----
>> From: owner-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com> 
>> <owner-nmus...@globomaxnm.com <mailto:owner-nmus...@globomaxnm.com>> On 
>> Behalf
>> Of Carlos ST
>> Sent: Wednesday, November 6, 2019 10:03 AM
>> To: nmusers@globomaxnm.com <mailto:nmusers@globomaxnm.com>
>> Subject: [NMusers] Using evid 0 before dosing
>> 
>> Dear NMUsers,
>> 
>> I would like advice in the best practice to use evid 0 before dosing, which
>> is to say an observation just before a dosing (*to estimate the value in
>> that compartment just before dosing event).
>> 
>> Thank you,
>> 
>> Carlos,

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