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RE: [NMusers] unbalanced data set

From: Zheng Liu <Zheng.Liu_at_rch.org.au>
Date: Mon, 25 Jan 2016 08:08:49 +0000

Dear Alison,



Thanks a lot for your detailed comments, which answered all my question. In=
 fact, I felt completely relieved after reading the methodology "BLUE" (Bes=
t Linear Unbiased Estimator). This is exactly what I expected. I guess now =
most of the users can use NONMEM delightfully, without worrying this issue.=
 Thank you also for introducing the development history of NONMEM.



Best regards,



Zheng Liu, Ph.D.
Pharmacometrician (postdoc), Melbourne Royal Children's Hospital
email: Zheng.Liu_at_rch.org.au
________________________________
From: Alison Boeckmann <alisonboeckmann_at_fastmail.fm>
Sent: Saturday, 23 January 2016 6:36 AM
To: Zheng Liu; nmusers_at_globomaxnm.com
Subject: Re: [NMusers] unbalanced data set

Nick's comment answered the question that was asked, although later
responses moved to a somewhat different subject.

I'd like to add a little history, as best as I remember what I was
told, that may illuminate the original issue, especially for
non-statisticians.

Prior to 1978, PK data was obtained from drugs that were tested on
healthy young volunteers (typically medical students). The data
was balanced, i.e., same number of samples at the same times from
each of them, typically over one day. If someone dropped out early,
it was generally for a reason un-related to the drug, and that
subject's data was simply ignored. A methodology such as ANOVA could
be used to analyze the data.

Lewis Sheiner objected to this. He said the drugs should be tested
on the target population. This sometimes meant sick people, in a
clinical setting, over a multi-visit time frame. If a subject
dropped out early, it might be because this person either over-responded
to the drug or under-responded and needed to be put on a rescue
medication. But these were the "outlier" subjects that the study
was most interested in! Lewis needed a way of combining unbalanced
data. Stuart Beal joined him in 1978. His PhD thesis was on a
technique for analyzing such data sets. By 1980, they released the
first version of NONMEM.

To make the point more clear:

At the Short Course, Stuart used to talk about a data set with 99
observed values of 100 and 1 observed value of 50. If there is no
other information, then the best estimate of the mean in the
population is a number close to 100. But what if you knew that the
99 values were from one subject, and the single value of 50 was
from a second subject? You'd be very sure of the value 100, but
much less sure about the value 50. Therefore, 75 would be a poor
choice for the mean in the population. There is a methodology
"BLUE" (Best Linear Unbiased Estimator). I can't remember what
Stuart said this gave, but it was a number between 75 and 100.

That is the whole idea behind NONMEM: to provide a weight
for each observation that takes into account the fact that
observations come from different subjects.

As Lewis says in Guide V,
"mixed effect modeling ... is especially useful when there are only
a few pharmacokinetic measurements from each individual sampled in
the population, or when the data collection design varies considerably
between these individuals."

-- Alison Boeckmann


On Tue, Jan 5, 2016, at 06:03 PM, Zheng Liu wrote:

Dear all,



I recently have a data set for pk parameters fitting. The issue is some pat=
ients have far more measurement points than others (i.e. a few patients hav=
e ~15 points, other patients have only 1 or 2). I speculate in the fitted p=
arameters, those patients with many points would contribute much more than =
those with less points. Then the population "average" values of fitted pk p=
arameters are not anymore average from all the patients, but more biased to=
 those patients with many points. This is not what I expect.



Of course I could take away some points from the patients with many points,=
 in order to be comparable to less-points patients. Then I will be forced =
to lose some information from the data set. I just wonder are there anyone =
who have better proposal to solve this problem? I appreciate your help very=
 much!



Best regards,



Zheng


--
  Alison Boeckmann
  alisonboeckmann_at_fastmail.fm




Received on Mon Jan 25 2016 - 03:08:49 EST

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