RE: BLQ Data
Hi Brady,
I think there are three main articles you can refer to in terms of using M3
method for BLQ data:
Likelihood based approaches to handling data below the quantification limit
using NONMEM VI.
http://www.ncbi.nlm.nih.gov/pubmed/18686017
Impact of low percentage of data below the quantification limit on parameter
estimates of pharmacokinetic models.
http://www.ncbi.nlm.nih.gov/pubmed/21626437
Handling data below the limit of quantification in mixed effect models.
http://www.ncbi.nlm.nih.gov/pubmed/19452283
As a general rule of thumb, most people tend to ignore(=exclude) the datapoints
(not the whole ID) if BLQ data are less than 10%.
However, I think it is important to look at where these BLQ data occur mainly
to know if they can bring more info into your model and sometimes,
having these extra BLQ data left in the analysis and using the M3 method can be
better for parameter estimation. Nonetheless beware that M3 method does not
always help in terms of run convergence (covariance step).
Hope it helps!
Best regards,
Camille
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Moffett, Brady S.
Sent: Friday, October 2, 2015 10:23 AM
To: [email protected]
Subject: [NMusers] BLQ Data
I am working with TDM data and am wondering when to use the M3 method for
dealing with BLQ data, or, when to just exclude data?
For instance: If I have <1% BLQ data in a large dataset (~1000 patients),
should I just exclude those patients/values? If I just have one patient with
one value BLQ, is it worth using the M3 method for that one patient? Is there
a breakpoint for which using these methods does/does not make a difference?
Thanks in advance...
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