RE: unbalanced design
Hi,
In Nick's example, the bias in disease progression parameters may indeed be
higher in the unbalanced design compared to the full, more extensive, design
in all subjects. However, that would in my mind come from data sparseness.
Bias would be expected to be even larger when all subjects have the sparser
design if for example the FOCE method is used. Whenever data per subject
becomes sparser, the FOCE method becomes more like the FO method and
therefore in general more biased in the parameter estimates.
Thus, robustness would decrease in the order "rich balanced design",
"rich+sparse unbalanced design", "sparse unbalanced design". Apart from this
effect I know of no reason to expect unbalanced designs not to be robust if
the model is correctly specified.
Best regards,
Mats
Mats Karlsson, PhD
Professor of Pharmacometrics
Dept of Pharmaceutical Biosciences
Uppsala University
Box 591
751 24 Uppsala Sweden
phone: +46 18 4714105
fax: +46 18 471 4003
Quoted reply history
-----Original Message-----
From: owner-nmusers
Behalf Of Nick Holford
Sent: Tuesday, September 02, 2008 10:03 PM
To: Wang, Yaning
Cc: Mark Sale - Next Level Solutions; nmusers
Subject: Re: [NMusers] unbalanced design
Hi,
Its not clear to me what Mark had in mind when he asked if " mixed
effect modeling (NONMEM in particular) is robust".
But Susan proposes its just obviously OK <grin> and Yaning suggests
reading a book for the simple case of linear models. But what about the
real world i.e. non-linear mixed models?
And surely there must be some degree of imbalance that would lead to a
non-robust description when using a mixed model? e.g. if one is trying
to described a disease progress curve and some people are followed long
enough to identify an exponential shape while others are followed for a
shorter time and appear to have a linear shape then wouldn't there be
some bias in the resulting estimates describing the curve depending on
the mix of short or long follow up times?
Nick
Willavize, Susan wrote:
Hi Mark,
This should be true just based on the nature of mixed effects modeling.
If you are not convinced, you may want to try some examples where you
simulate balanced and unbalanced designs and then estimate. J
Best Regard
Wang, Yaning wrote:
>
>
> Linear Mixed Models for Longitudinal Data by Geert Verbeke
>
http://www.amazon.com/exec/obidos/search-handle-url/102-2006236-4753744?%5F
encoding=UTF8&search-type=ss&index=books&field-author=Geert%20Verbeke,
> Geert Molenberghs
>
http://www.amazon.com/exec/obidos/search-handle-url/102-2006236-4753744?%5F
encoding=UTF8&search-type=ss&index=books&field-author=Geert%20Molenberghs
>
>
>
> Yaning Wang, Ph.D.
> Team Leader, Pharmacometrics
> Office of Clinical Pharmacology
> Office of Translational Science
> Center for Drug Evaluation and Research
> U.S. Food and Drug Administration
> Phone: 301-796-1624
> Email: yaning.wang
>
> "The contents of this message are mine personally and do not
> necessarily reflect any position of the Government or the Food and
> Drug Administration."
>
>
>
> ------------------------------------------------------------------------
> *From:* owner-nmusers
> [mailto:owner-nmusers
> Level Solutions
> *Sent:* Tuesday, September 02, 2008 1:28 PM
> *To:* nmusers
> *Subject:* [NMusers] unbalanced design
>
>
> Does anyone have a reference to a publication assessing whetheor
> unbalances studies? I see if in a number of courses (including the
> original beginners course for NONMEM), but can't find a publication.
> thanks
>
>
> Mark Sale MD
> Next Level Solutions, LLC
> www.NextLevelSolns.com http://www.NextLevelSolns.com
> 919-846-9185
>
--
Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
n.holford
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford